Prof. PhD DSc Eng
Leszek Rutkowski
Papers (200)
2024 (2)
Zhou Y., Lv W., Tao J., Xu Y., Huang T., Rutkowski L., Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel. (0)
Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel
, Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel, Neural Networks, 169, 169, 485-495, 2024, Cites: 0
Sevastjanov P., Kaczmarek K., Rutkowski L., A multi-model approach to the development of algorithmic trading systems for the Forex market. (0)
A multi-model approach to the development of algorithmic trading systems for the Forex market
, A multi-model approach to the development of algorithmic trading systems for the Forex market, Expert Systems with Applications, 236, 236, 2024, Cites: 02023 (26)
Duda P., Wojtulewicz M., Rutkowski L., The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach. (0)
The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach
, The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 46-55, 2023, Cites: 0
Wang J., Chen Z., Shen H., Cao J., Rutkowski L., Fuzzy <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_\infty$</tex-math></inline-formula> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems with Partial Information and Actuator Saturation. (1)
Fuzzy <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_\infty$</tex-math></inline-formula> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems with Partial Information and Actuator Saturation
, Fuzzy <inline-formula><tex-math notation="LaTeX">$\mathcal{H}_\infty$</tex-math></inline-formula> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems with Partial Information and Actuator Saturation, IEEE Transactions on Fuzzy Systems, 2023, Cites: 1
Lv X., Cao J., Rutkowski L., Duan P., Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-delay Multi-agent Systems with Switching Topologies. (0)
Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-delay Multi-agent Systems with Switching Topologies
, Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-delay Multi-agent Systems with Switching Topologies, IEEE Transactions on Automatic Control, 2023, Cites: 0
Krokosz T., Rykowski J., Zajecka M., Brzoza-Woch R., Rutkowski L., Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding. (0)
Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding
, Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding, Sensors, 23, 23, 2023, Cites: 0
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control. (3)
Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control
, Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control, IEEE Transactions on Automatic Control, 68, 68, 1215-1222, 2023, Cites: 3
Rutkowska D., Duda P., Cao J., Rutkowski L., Byrski A., Jaworski M., Tao D., The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks. (2)
The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks
, The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks, Information Sciences, 631, 631, 346-368, 2023, Cites: 2
Tao M., Guo L., Cao J., Rutkowski L., A Second Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems. (0)
A Second Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems
, A Second Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems, IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, Cites: 0
Izonin I., Tkachenko R., Gurbych O., Kovac M., Rutkowski L., Holoven R., A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis. (1)
A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis
, A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis, Mathematical Biosciences and Engineering, 20, 20, 13398-13414, 2023, Cites: 1
Shen H., Zhang Y., Wang J., Cao J., Rutkowski L., Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems. (0)
Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems
, Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems, IEEE Transactions on Automatic Control, 68, 68, 6255-6261, 2023, Cites: 0
Zhu S., Cao J., Lin L., Rutkowski L., Lu J., Lu G., Observability and Detectability of Stochastic Labeled Graphs. (0)
Observability and Detectability of Stochastic Labeled Graphs
, Observability and Detectability of Stochastic Labeled Graphs, IEEE Transactions on Automatic Control, 2023, Cites: 0
Huang Z., Lv W., Liu C., Xu Y., Rutkowski L., Huang T., Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks. (0)
Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks
, Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks, IEEE Transactions on Industrial Informatics, 2023, Cites: 0
Laktionov I., Rutkowski L., Vovna O., Byrski A., Kabanets M., A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques. (1)
A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques
, A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques, Engineering Applications of Artificial Intelligence, 126, 126, 2023, Cites: 1
Zhu W., Cao J., Shi X., Rutkowski L., Leader-following consensus of finite-field networks with time-delays. (0)
Leader-following consensus of finite-field networks with time-delays
, Leader-following consensus of finite-field networks with time-delays, Information Sciences, 647, 647, 2023, Cites: 0
Cheng J., Wu F., Liu L., Zhang Q., Rutkowski L., Tao D., InDecGAN: Learning to Generate Complex Images From Captions Via Independent Object-Level Decomposition and Enhancement. (0)
InDecGAN: Learning to Generate Complex Images From Captions Via Independent Object-Level Decomposition and Enhancement
, InDecGAN: Learning to Generate Complex Images From Captions Via Independent Object-Level Decomposition and Enhancement, IEEE Transactions on Multimedia, 2023, Cites: 0
Guan H., Liu Y., Kou K.I., Cao J., Rutkowski L., Collaborative neurodynamic optimization for solving nonlinear equations. (0)
Collaborative neurodynamic optimization for solving nonlinear equations
, Collaborative neurodynamic optimization for solving nonlinear equations, Neural Networks, 165, 165, 483-490, 2023, Cites: 0
Ghaffari R., Helfroush M.S., Khosravi A., Kazemi K., Danyali H., Rutkowski L., Toward domain adaptation with open-set target data: Review of theory and computer vision applications. (0)
Toward domain adaptation with open-set target data: Review of theory and computer vision applications
, Toward domain adaptation with open-set target data: Review of theory and computer vision applications, Information Fusion, 100, 100, 2023, Cites: 0
Starzec G., Starzec M., Bandyopadhyay S., Maulik U., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Two-Dimensional Pheromone in Ant Colony Optimization. (0)
Two-Dimensional Pheromone in Ant Colony Optimization
, Two-Dimensional Pheromone in Ant Colony Optimization, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14162 LNAI, 14162 LNAI, 459-471, 2023, Cites: 0
Wang Y., Yan J., Huang W., Rutkowski L., Cao J., Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track. (0)
Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track
, Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track, Science China Information Sciences, 66, 66, 2023, Cites: 0
Urbanczyk A., Kipinski P., Nabywaniec M., Rutkowski L., Chong S.Y., Yao X., Boryczko K., Byrski A., Socio-cognitive caste-based optimization. (0)
Socio-cognitive caste-based optimization
, Socio-cognitive caste-based optimization, Journal of Computational Science, 72, 72, 2023, Cites: 0
Wang J., Wu J., Shen H., Cao J., Rutkowski L., A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism. (0)
A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism
, A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 53, 4934-4943, 2023, Cites: 0
Lin A., Cheng J., Rutkowski L., Wen S., Luo M., Cao J., Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol. (11)
Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol
, Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol, IEEE Transactions on Neural Networks and Learning Systems, 34, 34, 9004-9015, 2023, Cites: 11
Feng Z.C., Xu W.Y., Cao J.D., Yang S.F., Rutkowski L., Distributed online bandit tracking for Nash equilibrium under partial-decision information setting. (0)
Distributed online bandit tracking for Nash equilibrium under partial-decision information setting
, Distributed online bandit tracking for Nash equilibrium under partial-decision information setting, Science China Technological Sciences, 66, 66, 3129-3138, 2023, Cites: 0
Wang J., Wu J., Shen H., Cao J., Rutkowski L., Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method. (5)
Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method
, Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method, IEEE Transactions on Cybernetics, 53, 53, 7380-7391, 2023, Cites: 5
Qi S., Wei W., Wang J., Sun S., Rutkowski L., Huang T., Kacprzyk J., Qi Y., Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage. (2)
Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage
, Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage, IEEE Transactions on Mobile Computing, 2023, Cites: 2
Zhang N., Wang J., Rutkowski L., Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization. (1)
Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization
, Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization, Neural Computing and Applications, 35, 35, 9947-9949, 2023, Cites: 1
Sevastjanov P., Kaczmarek K., Rutkowski L., A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization. (0)
A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization
, A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization, Applied Soft Computing, 147, 147, 2023, Cites: 02022 (17)
Luo Y., Zhu W., Cao J., Rutkowski L., Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems. (5)
Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems
, Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems, IEEE Transactions on Network Science and Engineering, 9, 9, 1527-1539, 2022, Cites: 5
Feng L., Liu L., Cao J., Rutkowski L., Lu G., General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching. (6)
General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching
, General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching, IEEE Transactions on Cybernetics, 52, 52, 5441-5453, 2022, Cites: 6
Xin Y., Cheng Z., Cao J., Rutkowski L., Wang Y., Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks. (4)
Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks
, Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2022, Cites: 4
Shen H., Wang X., Wang J., Cao J., Rutkowski L., Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method. (12)
Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method
, Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method, IEEE Transactions on Cybernetics, 52, 52, 12712-12721, 2022, Cites: 12
Tan X., Xiang C., Cao J., Xu W., Wen G., Rutkowski L., Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption. (31)
Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption
, Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption, IEEE Transactions on Cybernetics, 52, 52, 8246-8257, 2022, Cites: 31
Song Y., Cao J., Rutkowski L., A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy. (19)
A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy
, A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy, IEEE Transactions on Network Science and Engineering, 9, 9, 1154-1162, 2022, Cites: 19
Godzik M., Dajda J., Kisiel-Dorohinicki M., Byrski A., Rutkowski L., Orzechowski P., Wagenaar J., Moore J.H., Applying autonomous hybrid agent-based computing to difficult optimization problems. (0)
Applying autonomous hybrid agent-based computing to difficult optimization problems
, Applying autonomous hybrid agent-based computing to difficult optimization problems, Journal of Computational Science, 64, 64, 2022, Cites: 0
Feng Y., Zhang W., Xiong J., Li H., Rutkowski L., Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes. (7)
Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes
, Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes, IEEE Transactions on Cybernetics, 52, 52, 748-757, 2022, Cites: 7
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays. (14)
Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays
, Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays, IEEE Transactions on Circuits and Systems I: Regular Papers, 69, 69, 2095-2107, 2022, Cites: 14
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control. (12)
Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control
, Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control, IEEE Transactions on Cybernetics, 52, 52, 10290-10301, 2022, Cites: 12
Zhu W., Cao J., Shi X., Rutkowski L., Synchronization of Finite-Field Networks With Time Delays. (3)
Synchronization of Finite-Field Networks With Time Delays
, Synchronization of Finite-Field Networks With Time Delays, IEEE Transactions on Network Science and Engineering, 9, 9, 347-355, 2022, Cites: 3
Yu T., Cao J., Rutkowski L., Luo Y.-P., Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control. (25)
Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control
, Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 3938-3947, 2022, Cites: 25
Zhang N., Wang J., Rutkowski L., Editorial: Special Issue on Reliable Machine Learning and Optimization. (0)
Editorial: Special Issue on Reliable Machine Learning and Optimization
, Editorial: Special Issue on Reliable Machine Learning and Optimization, International Journal on Artificial Intelligence Tools, 31, 31, 2022, Cites: 0
Grycuk R., Galkowski T., Scherer R., Rutkowski L., A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder. (1)
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
, A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder, Proceedings of the International Joint Conference on Neural Networks, 2022-July, 2022-July, 2022, Cites: 1
Li Z., Tang Y., Fan Y., Huang T., Rutkowski L., Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses. (1)
Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses
, Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses, IEEE Transactions on Network Science and Engineering, 9, 9, 2224-2236, 2022, Cites: 1
Hu J., Cao J., Rutkowski L., Xue C., Yu J., Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators. (5)
Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators
, Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators, Electric Power Systems Research, 208, 208, 2022, Cites: 5
Staszewski P., Jaworski M., Cao J., Rutkowski L., A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers. (6)
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers
, A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 7913-7920, 2022, Cites: 62021 (11)
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks. (17)
A novel method for speed training acceleration of recurrent neural networks
, A novel method for speed training acceleration of recurrent neural networks, Information Sciences, 553, 553, 266-279, 2021, Cites: 17
Xia Z., Liu Y., Lu J., Cao J., Rutkowski L., Penalty method for constrained distributed quaternion-variable optimization. (37)
Penalty method for constrained distributed quaternion-variable optimization
, Penalty method for constrained distributed quaternion-variable optimization, IEEE Transactions on Cybernetics, 51, 51, 5631-5636, 2021, Cites: 37
Duda P., Rutkowski L., Woldan P., Najgebauer P., The Streaming Approach to Training Restricted Boltzmann Machines. (0)
The Streaming Approach to Training Restricted Boltzmann Machines
, The Streaming Approach to Training Restricted Boltzmann Machines, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 308-317, 2021, Cites: 0
Jaworski M., Rutkowski L., Staszewski P., Najgebauer P., Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines. (1)
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines
, Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 338-346, 2021, Cites: 1
Lv X., Cao J., Rutkowski L., Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control. (11)
Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control
, Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control, Neural Networks, 143, 143, 515-524, 2021, Cites: 11
He J., Liu Y., Lu J., Cao J., Rutkowski L., Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays. (4)
Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays
, Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 6842-6851, 2021, Cites: 4
Wang J., Yang C., Shen H., Cao J., Rutkowski L., Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters. (101)
Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters
, Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 7579-7586, 2021, Cites: 101
Tan X., Cao J., Rutkowski L., Lu G., Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution. (31)
Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution
, Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution, IEEE Transactions on Cybernetics, 51, 51, 624-634, 2021, Cites: 31
Yang X., Wan X., Zunshui C., Cao J., Liu Y., Rutkowski L., Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault. (66)
Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault
, Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault, IEEE Transactions on Neural Networks and Learning Systems, 32, 32, 4191-4201, 2021, Cites: 66
Xu S., Cao J., Liu Q., Rutkowski L., Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method. (6)
Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method
, Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 51, 3617-3628, 2021, Cites: 6
Xiong H., Tang Y.Y., Murtagh F., Rutkowski L., Berkovsky S., A diversified shared latent variable model for efficient image characteristics extraction and modelling. (2)
A diversified shared latent variable model for efficient image characteristics extraction and modelling
, A diversified shared latent variable model for efficient image characteristics extraction and modelling, Neurocomputing, 421, 421, 244-259, 2021, Cites: 22020 (30)
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria. (1)
Hybrid Splitting Criteria
, Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Regression. (0)
Regression
, Regression, Studies in Big Data, 56, 56, 309-322, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks. (1)
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
, Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks, Studies in Big Data, 56, 56, 173-244, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Splitting Criteria with the Bias Term. (0)
Splitting Criteria with the Bias Term
, Splitting Criteria with the Bias Term, Studies in Big Data, 56, 56, 83-89, 2020, Cites: 0
Rutkowski L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification. (2)
Probabilistic Neural Networks for the Streaming Data Classification
, Probabilistic Neural Networks for the Streaming Data Classification, Studies in Big Data, 56, 56, 245-277, 2020, Cites: 2
Rutkowski L., Jaworski M., Duda P., Introduction and Overview of the Main Results of the Book. (1)
Introduction and Overview of the Main Results of the Book
, Introduction and Overview of the Main Results of the Book, Studies in Big Data, 56, 56, 1-10, 2020, Cites: 1
Staszewski P., Jaworski M., Rutkowski L., Tao D., Explainable Cluster-Based Rules Generation for Image Retrieval and Classification. (1)
Explainable Cluster-Based Rules Generation for Image Retrieval and Classification
, Explainable Cluster-Based Rules Generation for Image Retrieval and Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 85-94, 2020, Cites: 1
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images. (4)
Fully Convolutional Network for Removing DCT Artefacts from Images
, Fully Convolutional Network for Removing DCT Artefacts from Images, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 4
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure. (1)
Misclassification Error Impurity Measure
, Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 1
Chen B., Cao J., Lu G., Rutkowski L., Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks. (20)
Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks
, Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks, IEEE Transactions on Circuits and Systems II: Express Briefs, 67, 67, 2537-2541, 2020, Cites: 20
Rutkowski L., Jaworski M., Duda P., Splitting Criteria Based on the McDiarmid’s Theorem. (0)
Splitting Criteria Based on the McDiarmid’s Theorem
, Splitting Criteria Based on the McDiarmid’s Theorem, Studies in Big Data, 56, 56, 51-62, 2020, Cites: 0
Lin L., Cao J., Rutkowski L., Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks. (35)
Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks
, Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1060-1065, 2020, Cites: 35
Cao J.-D., Liu Y., Lu J.-Q., Rutkowski L., Complex systems and networks with their applications. (1)
Complex systems and networks with their applications
, Complex systems and networks with their applications, Frontiers of Information Technology and Electronic Engineering, 21, 21, 195-198, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining. (7)
Decision Trees in Data Stream Mining
, Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 2020, Cites: 7
Rutkowski L., Jaworski M., Duda P., Final Remarks and Challenging Problems. (0)
Final Remarks and Challenging Problems
, Final Remarks and Challenging Problems, Studies in Big Data, 56, 56, 323-327, 2020, Cites: 0
Wang Z., Cao J., Cai Z., Rutkowski L., Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay. (60)
Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay
, Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay, IEEE Transactions on Cybernetics, 50, 50, 2758-2769, 2020, Cites: 60
Rutkowski L., Jaworski M., Duda P., Classification. (0)
Classification
, Classification, Studies in Big Data, 56, 56, 287-308, 2020, Cites: 0
Yang X., Liu Y., Cao J., Rutkowski L., Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching. (84)
Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching
, Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 5483-5496, 2020, Cites: 84
Li H., Fang J.-A., Li X., Rutkowski L., Huang T., Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays. (17)
Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays
, Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays, Neural Networks, 132, 132, 447-460, 2020, Cites: 17
Lin L., Cao J., Zhu S., Rutkowski L., Lu G., Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model. (23)
Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model
, Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model, IEEE Transactions on Control of Network Systems, 7, 7, 1859-1869, 2020, Cites: 23
Jaworski M., Rutkowski L., Angelov P., Concept Drift Detection Using Autoencoders in Data Streams Processing. (5)
Concept Drift Detection Using Autoencoders in Data Streams Processing
, Concept Drift Detection Using Autoencoders in Data Streams Processing, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 124-133, 2020, Cites: 5
Rutkowski L., Jaworski M., Duda P., General Non-parametric Learning Procedure for Tracking Concept Drift. (1)
General Non-parametric Learning Procedure for Tracking Concept Drift
, General Non-parametric Learning Procedure for Tracking Concept Drift, Studies in Big Data, 56, 56, 155-172, 2020, Cites: 1
Rutkowski L., Jaworski M., Duda P., The General Procedure of Ensembles Construction in Data Stream Scenarios. (0)
The General Procedure of Ensembles Construction in Data Stream Scenarios
, The General Procedure of Ensembles Construction in Data Stream Scenarios, Studies in Big Data, 56, 56, 281-286, 2020, Cites: 0
Liu Y., Zheng Y., Lu J., Cao J., Rutkowski L., Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach. (76)
Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach
, Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach, IEEE Transactions on Neural Networks and Learning Systems, 31, 31, 1022-1035, 2020, Cites: 76
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Probabilistic Neural Networks. (0)
Basic Concepts of Probabilistic Neural Networks
, Basic Concepts of Probabilistic Neural Networks, Studies in Big Data, 56, 56, 117-154, 2020, Cites: 0
Rutkowski T., Nielek R., Rutkowska D., Rutkowski L., A Novel Explainable Recommender for Investment Managers. (2)
A Novel Explainable Recommender for Investment Managers
, A Novel Explainable Recommender for Investment Managers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, 412-422, 2020, Cites: 2
Chen B., Cao J., Luo Y., Rutkowski L., Asymptotic Output Tracking of Probabilistic Boolean Control Networks. (24)
Asymptotic Output Tracking of Probabilistic Boolean Control Networks
, Asymptotic Output Tracking of Probabilistic Boolean Control Networks, IEEE Transactions on Circuits and Systems I: Regular Papers, 67, 67, 2780-2790, 2020, Cites: 24
Duda P., Rutkowski L., Jaworski M., Rutkowska D., On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification. (26)
On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification
, On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification, IEEE Transactions on Cybernetics, 50, 50, 1683-1696, 2020, Cites: 26
Tan X., Cao J., Rutkowski L., Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay. (51)
Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay
, Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay, IEEE Transactions on Network Science and Engineering, 7, 7, 1111-1120, 2020, Cites: 51
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Data Stream Mining. (17)
Basic Concepts of Data Stream Mining
, Basic Concepts of Data Stream Mining, Studies in Big Data, 56, 56, 13-33, 2020, Cites: 172019 (7)
Najgebauer P., Grycuk R., Rutkowski L., Scherer R., Siwocha A., Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection. (3)
Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection
, Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 164-171, 2019, Cites: 3
Rutkowska D., Rutkowski L., On the hermite series-based generalized regression neural networks for stream data mining. (1)
On the hermite series-based generalized regression neural networks for stream data mining
, On the hermite series-based generalized regression neural networks for stream data mining, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11955 LNCS, 11955 LNCS, 437-448, 2019, Cites: 1
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)). (0)
Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028))
, Corrigendum to ‘How to adjust an ensemble size in stream data mining?’ (Information Sciences (2017) 381 (46–54), (S0020025516313445) (10.1016/j.ins.2016.10.028)), Information Sciences, 477, 477, 545, 2019, Cites: 0
Jaworski M., Rutkowski L., Duda P., Cader A., Resource-aware data stream mining using the restricted boltzmann machine. (4)
Resource-aware data stream mining using the restricted boltzmann machine
, Resource-aware data stream mining using the restricted boltzmann machine, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, 384-396, 2019, Cites: 4
Feng L., Cao J., Hu J., Wu Z., Rutkowski L., Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode. (2)
Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode
, Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode, Neural Processing Letters, 50, 50, 2797-2819, 2019, Cites: 2
Woldan P., Staszewski P., Rutkowski L., Grzanek K., On Proper Designing of Deep Structures for Image Classification. (0)
On Proper Designing of Deep Structures for Image Classification
, On Proper Designing of Deep Structures for Image Classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 223-235, 2019, Cites: 0
Jaworski M., Duda P., Rutkowska D., Rutkowski L., On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine. (1)
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine
, On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine, Communications in Computer and Information Science, 1143 CCIS, 1143 CCIS, 347-354, 2019, Cites: 12018 (11)
Jaworski M., Duda P., Rutkowski L., Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine. (12)
Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine
, Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine, Proceedings of the International Joint Conference on Neural Networks, 2018-July, 2018-July, 2018, Cites: 12
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, V-VI, 2018, Cites: 0
Duda P., Jaworski M., Rutkowski L., Online grnn-based ensembles for regression on evolving data streams. (6)
Online grnn-based ensembles for regression on evolving data streams
, Online grnn-based ensembles for regression on evolving data streams, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10878 LNCS, 10878 LNCS, 221-228, 2018, Cites: 6
Cao J., Rutkowski L., On the global convergence of the parzen-based generalized regression neural networks applied to streaming data. (1)
On the global convergence of the parzen-based generalized regression neural networks applied to streaming data
, On the global convergence of the parzen-based generalized regression neural networks applied to streaming data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 25-34, 2018, Cites: 1
Jaworski M., Duda P., Rutkowski L., New Splitting Criteria for Decision Trees in Stationary Data Streams. (81)
New Splitting Criteria for Decision Trees in Stationary Data Streams
, New Splitting Criteria for Decision Trees in Stationary Data Streams, IEEE Transactions on Neural Networks and Learning Systems, 29, 29, 2516-2529, 2018, Cites: 81
Duda P., Jaworski M., Rutkowski L., Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks. (24)
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences, 460-461, 460-461, 497-518, 2018, Cites: 24
Rutkowski T., Romanowski J., Woldan P., Staszewski P., Nielek R., Rutkowski L., A content-based recommendation system using neuro-fuzzy approach. (43)
A content-based recommendation system using neuro-fuzzy approach
, A content-based recommendation system using neuro-fuzzy approach, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 43
Zalasinski M., Cpalka K., Rutkowski L., Fuzzy-genetic approach to identity verification using a handwritten signature. (6)
Fuzzy-genetic approach to identity verification using a handwritten signature
, Fuzzy-genetic approach to identity verification using a handwritten signature, Studies in Computational Intelligence, 738, 738, 375-394, 2018, Cites: 6
Duda P., Jaworski M., Rutkowski L., Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks. (30)
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
, Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks, International Journal of Neural Systems, 28, 28, 2018, Cites: 30
Lapa K., Cpalka K., Rutkowski L., New aspects of interpretability of fuzzy systems for nonlinear modeling. (14)
New aspects of interpretability of fuzzy systems for nonlinear modeling
, New aspects of interpretability of fuzzy systems for nonlinear modeling, Studies in Computational Intelligence, 738, 738, 225-264, 2018, Cites: 14
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, V-VI, 2018, Cites: 02017 (6)
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?. (60)
How to adjust an ensemble size in stream data mining?
, How to adjust an ensemble size in stream data mining?, Information Sciences, 381, 381, 46-54, 2017, Cites: 60
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, v-vi, 2017, Cites: 0
Jaworski M., Duda P., Rutkowski L., Najgebauer P., Pawlak M., Heuristic regression function estimation methods for data streams with concept drift. (10)
Heuristic regression function estimation methods for data streams with concept drift
, Heuristic regression function estimation methods for data streams with concept drift, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 726-737, 2017, Cites: 10
Najgebauer P., Rutkowski L., Scherer R., Novel method for joining missing line fragments for medical image analysis. (3)
Novel method for joining missing line fragments for medical image analysis
, Novel method for joining missing line fragments for medical image analysis, 2017 22nd International Conference on Methods and Models in Automation and Robotics, MMAR 2017, 861-866, 2017, Cites: 3
Najgebauer P., Rutkowski L., Scherer R., Interest point localization based on edge detection according to gestalt laws. (2)
Interest point localization based on edge detection according to gestalt laws
, Interest point localization based on edge detection according to gestalt laws, 2017 2nd IEEE International Conference on Computational Intelligence and Applications, ICCIA 2017, 2017-January, 2017-January, 349-353, 2017, Cites: 2
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, V-VI, 2017, Cites: 02016 (8)
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., A method for automatic adjustment of ensemble size in stream data mining. (17)
A method for automatic adjustment of ensemble size in stream data mining
, A method for automatic adjustment of ensemble size in stream data mining, Proceedings of the International Joint Conference on Neural Networks, 2016-October, 2016-October, 9-15, 2016, Cites: 17
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, V-VI, 2016, Cites: 0
Nowicki R.K., Scherer R., Rutkowski L., Novel rough neural network for classification with missing data. (5)
Novel rough neural network for classification with missing data
, Novel rough neural network for classification with missing data, 2016 21st International Conference on Methods and Models in Automation and Robotics, MMAR 2016, 820-825, 2016, Cites: 5
Cpalka K., Zalasinski M., Rutkowski L., A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. (89)
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
, A new algorithm for identity verification based on the analysis of a handwritten dynamic signature, Applied Soft Computing Journal, 43, 43, 47-56, 2016, Cites: 89
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers. (136)
Fast image classification by boosting fuzzy classifiers
, Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 136
Jaworski M., Rutkowski L., Pawlak M., Hybrid splitting criterion in decision trees for data stream mining. (6)
Hybrid splitting criterion in decision trees for data stream mining
, Hybrid splitting criterion in decision trees for data stream mining, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 60-72, 2016, Cites: 6
Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M., Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I. (0)
Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I
, Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 2016, Cites: 0
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, V-VI, 2016, Cites: 02015 (5)
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, V-VII, 2015, Cites: 0
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., A new method for data stream mining based on the misclassification error. (95)
A new method for data stream mining based on the misclassification error
, A new method for data stream mining based on the misclassification error, IEEE Transactions on Neural Networks and Learning Systems, 26, 26, 1048-1059, 2015, Cites: 95
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, V-VII, 2015, Cites: 0
Szarek A., Korytkowski M., Rutkowski L., Scherer M., Szyprowski J., Kostadinov D., Customization of joint articulations using soft computing methods. (1)
Customization of joint articulations using soft computing methods
, Customization of joint articulations using soft computing methods, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 151-160, 2015, Cites: 1
Najgebauer P., Rygal J., Nowak T., Romanowski J., Rutkowski L., Voloshynovskiy S., Scherer R., Fast dictionary matching for content-based image retrieval. (2)
Fast dictionary matching for content-based image retrieval
, Fast dictionary matching for content-based image retrieval, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 747-756, 2015, Cites: 22014 (5)
Cpalka K., Zalasinski M., Rutkowski L., New method for the on-line signature verification based on horizontal partitioning. (81)
New method for the on-line signature verification based on horizontal partitioning
, New method for the on-line signature verification based on horizontal partitioning, Pattern Recognition, 47, 47, 2652-2661, 2014, Cites: 81
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., The CART decision tree for mining data streams. (241)
The CART decision tree for mining data streams
, The CART decision tree for mining data streams, Information Sciences, 266, 266, 1-15, 2014, Cites: 241
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., Decision trees for mining data streams based on the gaussian approximation. (138)
Decision trees for mining data streams based on the gaussian approximation
, Decision trees for mining data streams based on the gaussian approximation, IEEE Transactions on Knowledge and Data Engineering, 26, 26, 108-119, 2014, Cites: 138
Duda P., Jaworski M., Pietruczuk L., Rutkowski L., A novel application of Hoeffding's inequality to decision trees construction for data streams. (14)
A novel application of Hoeffding's inequality to decision trees construction for data streams
, A novel application of Hoeffding's inequality to decision trees construction for data streams, Proceedings of the International Joint Conference on Neural Networks, 3324-3330, 2014, Cites: 14
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., The Parzen kernel approach to learning in non-stationary environment. (11)
The Parzen kernel approach to learning in non-stationary environment
, The Parzen kernel approach to learning in non-stationary environment, Proceedings of the International Joint Conference on Neural Networks, 3319-3323, 2014, Cites: 112013 (3)
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On design of flexible neuro-fuzzy systems for nonlinear modelling. (66)
On design of flexible neuro-fuzzy systems for nonlinear modelling
, On design of flexible neuro-fuzzy systems for nonlinear modelling, International Journal of General Systems, 42, 42, 706-720, 2013, Cites: 66
Gabryel M., Korytkowski M., Scherer R., Rutkowski L., Object detection by simple fuzzy classifiers generated by boosting. (24)
Object detection by simple fuzzy classifiers generated by boosting
, Object detection by simple fuzzy classifiers generated by boosting, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 540-547, 2013, Cites: 24
Rutkowski L., Pietruczuk L., Duda P., Jaworski M., Decision trees for mining data streams based on the mcdiarmid's bound. (161)
Decision trees for mining data streams based on the mcdiarmid's bound
, Decision trees for mining data streams based on the mcdiarmid's bound, IEEE Transactions on Knowledge and Data Engineering, 25, 25, 1272-1279, 2013, Cites: 1612012 (6)
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Forecasting wear of head and acetabulum in hip joint implant. (14)
Forecasting wear of head and acetabulum in hip joint implant
, Forecasting wear of head and acetabulum in hip joint implant, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 341-346, 2012, Cites: 14
Rutkowski L., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface. (0)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7269 LNCS, 7269 LNCS, 2012, Cites: 0
Rutkowski L., Przybyl A., Cpalka K., Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. (89)
Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation
, Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation, IEEE Transactions on Industrial Electronics, 59, 59, 1238-1247, 2012, Cites: 89
Rutkowski L., Cpalka K., Nowicki R., Pokropinska A., Scherer R., Neuro-fuzzy systems. (4)
Neuro-fuzzy systems
, Neuro-fuzzy systems, Computational Complexity: Theory, Techniques, and Applications, 9781461418009, 9781461418009, 2069-2081, 2012, Cites: 4
Rutkowski L., Preface. (0)
Preface
, Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7267 LNAI, 7267 LNAI, 2012, Cites: 0
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., Application of neural networks in assessing changes around implant after total hip arthroplasty. (23)
Application of neural networks in assessing changes around implant after total hip arthroplasty
, Application of neural networks in assessing changes around implant after total hip arthroplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 335-340, 2012, Cites: 232011 (4)
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., AdaBoost ensemble of DCOG rough-neuro-fuzzy systems. (25)
AdaBoost ensemble of DCOG rough-neuro-fuzzy systems
, AdaBoost ensemble of DCOG rough-neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6922 LNAI, 6922 LNAI, 62-71, 2011, Cites: 25
Rutkowski L., Foreword. (0)
Foreword
, Foreword, Intelligent Systems Reference Library, 6, 6, 2011, Cites: 0
Korytkowski M., Rutkowski L., Scherer R., Rule base normalization in Takagi-Sugeno ensemble. (3)
Rule base normalization in Takagi-Sugeno ensemble
, Rule base normalization in Takagi-Sugeno ensemble, IEEE SSCI 2011 - Symposium Series on Computational Intelligence - HIMA 2011: 2011 IEEE Workshop on Hybrid Intelligent Models and Applications, 1-5, 2011, Cites: 3
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On designing of flexible neuro-fuzzy systems for nonlinear modelling. (2)
On designing of flexible neuro-fuzzy systems for nonlinear modelling
, On designing of flexible neuro-fuzzy systems for nonlinear modelling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6743 LNAI, 6743 LNAI, 147-154, 2011, Cites: 22010 (9)
Gabryel M., Rutkowski L., Evolutionary designing of logic-type fuzzy systems. (5)
Evolutionary designing of logic-type fuzzy systems
, Evolutionary designing of logic-type fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 143-148, 2010, Cites: 5
Korytkowski M., Nowicki R.K., Scherer R., Rutkowski L., MICOG defuzzification rough-neuro-fuzzy system ensemble. (5)
MICOG defuzzification rough-neuro-fuzzy system ensemble
, MICOG defuzzification rough-neuro-fuzzy system ensemble, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010, Cites: 5
Du J., Er M.J., Rutkowski L., Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach. (6)
Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach
, Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 58-65, 2010, Cites: 6
Korytkowski M., Rutkowski L., Scherer R., Szarek A., Neural network-based assessment of femur stress after hip joint alloplasty. (0)
Neural network-based assessment of femur stress after hip joint alloplasty
, Neural network-based assessment of femur stress after hip joint alloplasty, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 621-626, 2010, Cites: 0
Li X., Er M.J., Lim B.S., Zhou J.H., Gan O.P., Rutkowski L., Fuzzy regression modeling for tool performance prediction and degradation detection. (64)
Fuzzy regression modeling for tool performance prediction and degradation detection
, Fuzzy regression modeling for tool performance prediction and degradation detection, International Journal of Neural Systems, 20, 20, 405-419, 2010, Cites: 64
Cpalka K., Rutkowski L., Er M.J., On automatic design of neuro-fuzzy systems. (0)
On automatic design of neuro-fuzzy systems
, On automatic design of neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 43-48, 2010, Cites: 0
Liu F., Er M.J., Rutkowski L., An online approach towards self-generating fuzzy neural networks with applications. (0)
An online approach towards self-generating fuzzy neural networks with applications
, An online approach towards self-generating fuzzy neural networks with applications, Proceedings of the International Joint Conference on Neural Networks, 2010, Cites: 0
Rutkowski L., Przybyl A., Cpalka K., Er M.J., Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. (53)
Online speed profile generation for industrial machine tool based on neuro-fuzzy approach
, Online speed profile generation for industrial machine tool based on neuro-fuzzy approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 645-650, 2010, Cites: 53
Du J., Er M.J., Rutkowski L., An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting. (3)
An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting
, An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6113 LNAI, 6113 LNAI, 49-57, 2010, Cites: 32009 (1)
Cpalka K., Rebrova O., Rutkowski L., A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis. (2)
A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis
, A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5769 LNCS, 5769 LNCS, 435-444, 2009, Cites: 22008 (10)
Korytkowski M., Rutkowski L., Scherer R., From ensemble of fuzzy classifiers to single fuzzy rule base classifier. (61)
From ensemble of fuzzy classifiers to single fuzzy rule base classifier
, From ensemble of fuzzy classifiers to single fuzzy rule base classifier, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 265-272, 2008, Cites: 61
Rutkowski L., Lecture Notes in Artificial Intelligence : Preface. (0)
Lecture Notes in Artificial Intelligence : Preface
, Lecture Notes in Artificial Intelligence : Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 2008, Cites: 0
Cpalka K., Rutkowski L., An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems. (1)
An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems
, An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 207-216, 2008, Cites: 1
Rutkowski L., Computational intelligence: Methods and techniques. (301)
Computational intelligence: Methods and techniques
, Computational intelligence: Methods and techniques, Computational Intelligence: Methods and Techniques, 1-514, 2008, Cites: 301
Cpalka K., Rutkowski L., Evolutionary learning of flexible neuro-fuzzy systems. (10)
Evolutionary learning of flexible neuro-fuzzy systems
, Evolutionary learning of flexible neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 969-975, 2008, Cites: 10
Korytkowski M., Gabryel M., Rutkowski L., Drozda S., Evolutionary methods to create interpretable modular system. (13)
Evolutionary methods to create interpretable modular system
, Evolutionary methods to create interpretable modular system, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 405-413, 2008, Cites: 13
Cpalka K., Rebrova O., Galkowski T., Rutkowski L., On differential stroke diagnosis by neuro-fuzzy structures. (1)
On differential stroke diagnosis by neuro-fuzzy structures
, On differential stroke diagnosis by neuro-fuzzy structures, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 974-980, 2008, Cites: 1
Korytkowski M., Nowicki R., Scherer R., Rutkowski L., Ensemble of rough-neuro-fuzzy systems for classification with missing features. (13)
Ensemble of rough-neuro-fuzzy systems for classification with missing features
, Ensemble of rough-neuro-fuzzy systems for classification with missing features, IEEE International Conference on Fuzzy Systems, 1745-1750, 2008, Cites: 13
Gabryel M., Rutkowski L., Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm. (10)
Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm
, Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 398-404, 2008, Cites: 10
Scherer R., Korytkowski M., Nowicki R., Rutkowski L., Modular rough neuro-fuzzy systems for classification. (3)
Modular rough neuro-fuzzy systems for classification
, Modular rough neuro-fuzzy systems for classification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4967 LNCS, 4967 LNCS, 540-548, 2008, Cites: 32007 (3)
Korytkowski M., Rutkowski L., Scherer R., On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm. (0)
On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm
, On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm, Advances in Soft Computing, 45, 45, 319-326, 2007, Cites: 0
Korytkowski M., Rutkowski L., Scherer R., Drozda G., On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems. (1)
On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems
, On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems, Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007, 234-237, 2007, Cites: 1
Cpalka K., Nowicki R., Rutkowski L., Rough-neuro-fuzzy systems for classification. (3)
Rough-neuro-fuzzy systems for classification
, Rough-neuro-fuzzy systems for classification, Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 1-8, 2007, Cites: 32006 (6)
Cpalka K., Rutkowski L., A new method for designing and reduction of neuro-fuzzy systems. (16)
A new method for designing and reduction of neuro-fuzzy systems
, A new method for designing and reduction of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1851-1857, 2006, Cites: 16
Gabryel M., Rutkowski L., Evolutionary learning of mamdani-type neuro-fuzzy systems. (19)
Evolutionary learning of mamdani-type neuro-fuzzy systems
, Evolutionary learning of mamdani-type neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 354-359, 2006, Cites: 19
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Merging ensemble of neuro-fuzzy systems. (7)
Merging ensemble of neuro-fuzzy systems
, Merging ensemble of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1954-1957, 2006, Cites: 7
Korytkowski M., Rutkowski L., Scherer R., On combining backpropagation with boosting. (58)
On combining backpropagation with boosting
, On combining backpropagation with boosting, IEEE International Conference on Neural Networks - Conference Proceedings, 1274-1277, 2006, Cites: 58
Cpalka K., Rutkowski L., A new method for complexity reduction of neuro-fuzzy systems. (1)
A new method for complexity reduction of neuro-fuzzy systems
, A new method for complexity reduction of neuro-fuzzy systems, WSEAS Transactions on Systems, 5, 5, 2514-2521, 2006, Cites: 1
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Combining logical-type neuro-fuzzy systems. (1)
Combining logical-type neuro-fuzzy systems
, Combining logical-type neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 240-249, 2006, Cites: 12005 (4)
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation. (42)
Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation
, Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation, WSEAS Transactions on Systems, 4, 4, 1450-1458, 2005, Cites: 42
Rutkowski L., Cpalka K., Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems. (67)
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
, Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems, IEEE Transactions on Fuzzy Systems, 13, 13, 140-151, 2005, Cites: 67
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno fuzzy systems. (56)
Flexible Takagi-Sugeno fuzzy systems
, Flexible Takagi-Sugeno fuzzy systems, Proceedings of the International Joint Conference on Neural Networks, 3, 3, 1764-1769, 2005, Cites: 56
Cpalka K., Rutkowski L., Flexible neuro-fuzzy structures for pattern classification. (7)
Flexible neuro-fuzzy structures for pattern classification
, Flexible neuro-fuzzy structures for pattern classification, WSEAS Transactions on Computers, 4, 4, 679-688, 2005, Cites: 72004 (6)
Scherer R., Rutkowski L., Neuro-fuzzy relational classifiers. (23)
Neuro-fuzzy relational classifiers
, Neuro-fuzzy relational classifiers, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 376-380, 2004, Cites: 23
Rutkowski L., Adaptive probabilistic neural networks for pattern classification in time-varying environment. (196)
Adaptive probabilistic neural networks for pattern classification in time-varying environment
, Adaptive probabilistic neural networks for pattern classification in time-varying environment, IEEE Transactions on Neural Networks, 15, 15, 811-827, 2004, Cites: 196
Rutkowski L., Cpalka K., Neuro-fuzzy systems derived from quasi-triangular norms. (39)
Neuro-fuzzy systems derived from quasi-triangular norms
, Neuro-fuzzy systems derived from quasi-triangular norms, IEEE International Conference on Fuzzy Systems, 2, 2, 1031-1036, 2004, Cites: 39
Cpalka K., Rutkowski L., Fuzzy modelling with a compromise fuzzy reasoning. (0)
Fuzzy modelling with a compromise fuzzy reasoning
, Fuzzy modelling with a compromise fuzzy reasoning, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 284-289, 2004, Cites: 0
Rutkowski L., A new method for system modelling and pattern classification. (18)
A new method for system modelling and pattern classification
, A new method for system modelling and pattern classification, Bulletin of the Polish Academy of Sciences: Technical Sciences, 52, 52, 11-24, 2004, Cites: 18
Rutkowski L., Generalized regression neural networks in time-varying environment. (91)
Generalized regression neural networks in time-varying environment
, Generalized regression neural networks in time-varying environment, IEEE Transactions on Neural Networks, 15, 15, 576-596, 2004, Cites: 912003 (4)
Nowicki R., Scherer R., Rutkowski L., A Hierarchical Neuro-Fuzzy System Based on S-Implications. (5)
A Hierarchical Neuro-Fuzzy System Based on S-Implications
, A Hierarchical Neuro-Fuzzy System Based on S-Implications, Proceedings of the International Joint Conference on Neural Networks, 1, 1, 321-325, 2003, Cites: 5
Rutkowski L., Cpalka K., Flexible neuro-fuzzy systems. (180)
Flexible neuro-fuzzy systems
, Flexible neuro-fuzzy systems, IEEE Transactions on Neural Networks, 14, 14, 554-574, 2003, Cites: 180
Rutkowski L., Cpalka K., Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574)). (0)
Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574))
, Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574)), IEEE Transactions on Neural Networks, 14, 14, 967, 2003, Cites: 0
Rutkowski L., Cpalka K., A new approach to designing fuzzy systems. (0)
A new approach to designing fuzzy systems
, A new approach to designing fuzzy systems, Recent Advances in Intelligent Systems and Signal Processing, 343-347, 2003, Cites: 02002 (3)
Rutkowski L., Cpalka K., Flexible weighted neuro-fuzzy systems. (36)
Flexible weighted neuro-fuzzy systems
, Flexible weighted neuro-fuzzy systems, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, 4, 4, 1857-1861, 2002, Cites: 36
Starczewski J., Rutkowski L., Connectionist structures of type 2 fuzzy inference systems. (62)
Connectionist structures of type 2 fuzzy inference systems
, Connectionist structures of type 2 fuzzy inference systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2328, 2328, 634-642, 2002, Cites: 62
Rutkowski L., Cpalka K., A neuro - Fuzzy controller with a compromise fuzzy reasoning. (56)
A neuro - Fuzzy controller with a compromise fuzzy reasoning
, A neuro - Fuzzy controller with a compromise fuzzy reasoning, Control and Cybernetics, 31, 31, 297-308, 2002, Cites: 562001 (2)
Rutkowska D., Rutkowski L., Nowicki R., Neuro-fuzzy systems with inference based on bounded product. (5)
Neuro-fuzzy systems with inference based on bounded product
, Neuro-fuzzy systems with inference based on bounded product, Advances in Neural Networks and Applications, 104-109, 2001, Cites: 5
Rutkowski L., Cpalka K., A general approach to neuro-fuzzy systems. (47)
A general approach to neuro-fuzzy systems
, A general approach to neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 3, 3, 1428-1431, 2001, Cites: 472000 (1)
Cierniak R., Rutkowski L., On image compression by competitive neural networks and optimal linear predictors. (51)
On image compression by competitive neural networks and optimal linear predictors
, On image compression by competitive neural networks and optimal linear predictors, Signal Processing: Image Communication, 15, 15, 559-565, 2000, Cites: 511998 (1)
Bilski J., Rutkowski L., A fast training algorithm for neural networks. (64)
A fast training algorithm for neural networks
, A fast training algorithm for neural networks, IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 45, 45, 749-753, 1998, Cites: 641996 (1)
Cierniak Robert, Rutkowski Leszek, Neural networks and semi-closed-loop predictive vector quantization for image compression. (1)
Neural networks and semi-closed-loop predictive vector quantization for image compression
, Neural networks and semi-closed-loop predictive vector quantization for image compression, IEEE International Conference on Image Processing, 1, 1, 245-248, 1996, Cites: 11993 (1)
Rutkowski L., Multiple Fourier Series Procedures for Extraction of Nonlinear Regressions from Noisy Data. (52)
Multiple Fourier Series Procedures for Extraction of Nonlinear Regressions from Noisy Data
, Multiple Fourier Series Procedures for Extraction of Nonlinear Regressions from Noisy Data, IEEE Transactions on Signal Processing, 41, 41, 3062-3065, 1993, Cites: 521991 (1)
Rutkowski L., Identification of MISO Nonlinear Regressions in the Presence of a Wide Class of Disturbances. (53)
Identification of MISO Nonlinear Regressions in the Presence of a Wide Class of Disturbances
, Identification of MISO Nonlinear Regressions in the Presence of a Wide Class of Disturbances, IEEE Transactions on Information Theory, 37, 37, 214-216, 1991, Cites: 531989 (5)
Rutkowski L., Application of multiple fourier series to identification of multivariable non-stationary systems. (54)
Application of multiple fourier series to identification of multivariable non-stationary systems
, Application of multiple fourier series to identification of multivariable non-stationary systems, International Journal of Systems Science, 20, 20, 1993-2002, 1989, Cites: 54
Kozietulski Marek, Rutkowski Leszek, Nonparametric procedure for identification of the step response function and its microprocessor implementation. (1)
Nonparametric procedure for identification of the step response function and its microprocessor implementation
, Nonparametric procedure for identification of the step response function and its microprocessor implementation, Advances in modelling & simulation, 17, 17, 25-36, 1989, Cites: 1
Rutkowski L., Non-parametric learning algorithms in time-varying environments. (50)
Non-parametric learning algorithms in time-varying environments
, Non-parametric learning algorithms in time-varying environments, Signal Processing, 18, 18, 129-137, 1989, Cites: 50
Rafajlowicz Ewaryst, Rutkowski Leszek, Nonparametric identification of input signals in distributed systems. (0)
Nonparametric identification of input signals in distributed systems
, Nonparametric identification of input signals in distributed systems, Proceedings of the IEEE Conference on Decision and Control, 2, 2, 1464-1465, 1989, Cites: 0
Rutkowski L., Rafajlowicz E., On Optimal Global Rate of Convergence of Some Nonparametric Identification Procedures. (59)
On Optimal Global Rate of Convergence of Some Nonparametric Identification Procedures
, On Optimal Global Rate of Convergence of Some Nonparametric Identification Procedures, IEEE Transactions on Automatic Control, 34, 34, 1089-1091, 1989, Cites: 591988 (1)
Rutkowski L., Sequential pattern recognition procedures derived from multiple Fourier series. (52)
