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Ex-Staff Member

Prof. PhD DSc Eng Leszek Rutkowski

Papers (200)

2024 (2)

Event-triggered impulsive quasi-synchronization for BAM neural networks with reliable redundant channel
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, Neural Networks, 169, 169, 485-495, 2024, Cites: 0
A multi-model approach to the development of algorithmic trading systems for the Forex market
Sevastjanov P., Kaczmarek K., Rutkowski L., A multi-model approach to the development of algorithmic trading systems for the Forex market, Expert Systems with Applications, 236, 236, 2024, Cites: 0

2023 (26)

The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach
Duda P., Wojtulewicz M., Rutkowski L., 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
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
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, IEEE Transactions on Fuzzy Systems, 2023, Cites: 1
Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-delay Multi-agent Systems with Switching Topologies
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, IEEE Transactions on Automatic Control, 2023, Cites: 0
Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding
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, Sensors, 23, 23, 2023, Cites: 0
Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Event-Triggered Control, IEEE Transactions on Automatic Control, 68, 68, 1215-1222, 2023, Cites: 3
The L<inf>2</inf> convergence of stream data mining algorithms based on probabilistic neural networks
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, Information Sciences, 631, 631, 346-368, 2023, Cites: 2
A Second Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems
Tao M., Guo L., Cao J., Rutkowski L., A Second Order Primal-Dual Dynamics for Set Constrained Distributed Optimization Problems, IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, Cites: 0
A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis
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, Mathematical Biosciences and Engineering, 20, 20, 13398-13414, 2023, Cites: 1
Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems
Shen H., Zhang Y., Wang J., Cao J., Rutkowski L., Observer-Based Control for Discrete-Time Hidden Semi-Markov Jump Systems, IEEE Transactions on Automatic Control, 68, 68, 6255-6261, 2023, Cites: 0
Observability and Detectability of Stochastic Labeled Graphs
Zhu S., Cao J., Lin L., Rutkowski L., Lu J., Lu G., Observability and Detectability of Stochastic Labeled Graphs, IEEE Transactions on Automatic Control, 2023, Cites: 0
Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks
Huang Z., Lv W., Liu C., Xu Y., Rutkowski L., Huang T., Event-Triggered Distributed Moving Horizon Estimation Over Wireless Sensor Networks, IEEE Transactions on Industrial Informatics, 2023, Cites: 0
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
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, Engineering Applications of Artificial Intelligence, 126, 126, 2023, Cites: 1
Leader-following consensus of finite-field networks with time-delays
Zhu W., Cao J., Shi X., Rutkowski L., Leader-following consensus of finite-field networks with time-delays, Information Sciences, 647, 647, 2023, Cites: 0
InDecGAN: Learning to Generate Complex Images From Captions Via Independent Object-Level Decomposition and Enhancement
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, IEEE Transactions on Multimedia, 2023, Cites: 0
Collaborative neurodynamic optimization for solving nonlinear equations
Guan H., Liu Y., Kou K.I., Cao J., Rutkowski L., Collaborative neurodynamic optimization for solving nonlinear equations, Neural Networks, 165, 165, 483-490, 2023, Cites: 0
Toward domain adaptation with open-set target data: Review of theory and computer vision applications
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, Information Fusion, 100, 100, 2023, Cites: 0
Two-Dimensional Pheromone in Ant Colony Optimization
Starzec G., Starzec M., Bandyopadhyay S., Maulik U., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., 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
Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track
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, Science China Information Sciences, 66, 66, 2023, Cites: 0
Socio-cognitive caste-based optimization
Urbanczyk A., Kipinski P., Nabywaniec M., Rutkowski L., Chong S.Y., Yao X., Boryczko K., Byrski A., Socio-cognitive caste-based optimization, Journal of Computational Science, 72, 72, 2023, Cites: 0
A Decentralized Learning Control Scheme for Constrained Nonlinear Interconnected Systems Based on Dynamic Event-Triggered Mechanism
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, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 53, 4934-4943, 2023, Cites: 0
Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol
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, IEEE Transactions on Neural Networks and Learning Systems, 34, 34, 9004-9015, 2023, Cites: 11
Distributed online bandit tracking for Nash equilibrium under partial-decision information setting
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, Science China Technological Sciences, 66, 66, 3129-3138, 2023, Cites: 0
Fuzzy H<inf>∞</inf> Control of Discrete-Time Nonlinear Markov Jump Systems via a Novel Hybrid Reinforcement Q-Learning Method
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, IEEE Transactions on Cybernetics, 53, 53, 7380-7391, 2023, Cites: 5
Secure Data Deduplication With Dynamic Access Control for Mobile Cloud Storage
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, IEEE Transactions on Mobile Computing, 2023, Cites: 2
Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization
Zhang N., Wang J., Rutkowski L., Special issue on deep interpretation of deep learning: prediction, representation, modeling and utilization, Neural Computing and Applications, 35, 35, 9947-9949, 2023, Cites: 1
A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization
Sevastjanov P., Kaczmarek K., Rutkowski L., A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization, Applied Soft Computing, 147, 147, 2023, Cites: 0

2022 (17)

Event-Triggered Finite-Time Guaranteed Cost H-Infinity Consensus for Nonlinear Uncertain Multi-Agent Systems
Luo Y., Zhu W., Cao J., Rutkowski L., 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
General Decay Stability for Nonautonomous Neutral Stochastic Systems with Time-Varying Delays and Markovian Switching
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, IEEE Transactions on Cybernetics, 52, 52, 5441-5453, 2022, Cites: 6
Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks
Xin Y., Cheng Z., Cao J., Rutkowski L., Wang Y., Circuit Implementation and Quasi-Stabilization of Delayed Inertial Memristor-Based Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, 2022, Cites: 4
Robust Composite H<inf>∞</inf>Synchronization of Markov Jump Reaction-Diffusion Neural Networks via a Disturbance Observer-Based Method
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, IEEE Transactions on Cybernetics, 52, 52, 12712-12721, 2022, Cites: 12
Synchronization of Neural Networks via Periodic Self-Triggered Impulsive Control and Its Application in Image Encryption
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, IEEE Transactions on Cybernetics, 52, 52, 8246-8257, 2022, Cites: 31
A Fixed-Time Distributed Optimization Algorithm Based on Event-Triggered Strategy
Song Y., Cao J., Rutkowski L., 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
Applying autonomous hybrid agent-based computing to difficult optimization problems
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, Journal of Computational Science, 64, 64, 2022, Cites: 0
Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes
Feng Y., Zhang W., Xiong J., Li H., Rutkowski L., Event-Triggering Interaction Scheme for Discrete-Time Decentralized Optimization with Nonuniform Step Sizes, IEEE Transactions on Cybernetics, 52, 52, 748-757, 2022, Cites: 7
Event-Triggered Synchronization of Multiple Discrete-Time Markovian Jump Memristor- Based Neural Networks With Mixed Mode-Dependent Delays
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, IEEE Transactions on Circuits and Systems I: Regular Papers, 69, 69, 2095-2107, 2022, Cites: 14
Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control
Chen B., Cao J., Lu G., Rutkowski L., Stabilization of Markovian Jump Boolean Control Networks via Sampled-Data Control, IEEE Transactions on Cybernetics, 52, 52, 10290-10301, 2022, Cites: 12
Synchronization of Finite-Field Networks With Time Delays
Zhu W., Cao J., Shi X., Rutkowski L., Synchronization of Finite-Field Networks With Time Delays, IEEE Transactions on Network Science and Engineering, 9, 9, 347-355, 2022, Cites: 3
Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control
Yu T., Cao J., Rutkowski L., Luo Y.-P., 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
Editorial: Special Issue on Reliable Machine Learning and Optimization
Zhang N., Wang J., Rutkowski L., Editorial: Special Issue on Reliable Machine Learning and Optimization, International Journal on Artificial Intelligence Tools, 31, 31, 2022, Cites: 0
A Novel Method for Solar Image Retrieval Based on the Parzen Kernel Estimate of the Function Derivative and Convolutional Autoencoder
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, Proceedings of the International Joint Conference on Neural Networks, 2022-July, 2022-July, 2022, Cites: 1
Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses
Li Z., Tang Y., Fan Y., Huang T., Rutkowski L., Formation Control of Multi-Agent Systems With Constrained Mismatched Compasses, IEEE Transactions on Network Science and Engineering, 9, 9, 2224-2236, 2022, Cites: 1
Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators
Hu J., Cao J., Rutkowski L., Xue C., Yu J., Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators, Electric Power Systems Research, 208, 208, 2022, Cites: 5
A New Approach to Descriptors Generation for Image Retrieval by Analyzing Activations of Deep Neural Network Layers
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, IEEE Transactions on Neural Networks and Learning Systems, 33, 33, 7913-7920, 2022, Cites: 6

2021 (11)

A novel method for speed training acceleration of recurrent neural networks
Bilski J., Rutkowski L., Smolag J., Tao D., A novel method for speed training acceleration of recurrent neural networks, Information Sciences, 553, 553, 266-279, 2021, Cites: 17
Penalty method for constrained distributed quaternion-variable optimization
Xia Z., Liu Y., Lu J., Cao J., Rutkowski L., Penalty method for constrained distributed quaternion-variable optimization, IEEE Transactions on Cybernetics, 51, 51, 5631-5636, 2021, Cites: 37
The Streaming Approach to Training Restricted Boltzmann Machines
Duda P., Rutkowski L., Woldan P., Najgebauer P., 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
Monitoring of Changes in Data Stream Distribution Using Convolutional Restricted Boltzmann Machines
Jaworski M., Rutkowski L., Staszewski P., Najgebauer P., 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
Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control
Lv X., Cao J., Rutkowski L., Dynamical and static multisynchronization analysis for coupled multistable memristive neural networks with hybrid control, Neural Networks, 143, 143, 515-524, 2021, Cites: 11
Event-Triggered Control for Output Regulation of Probabilistic Logical Systems with Delays
He J., Liu Y., Lu J., Cao J., Rutkowski L., 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
Sliding-Mode Control for Slow-Sampling Singularly Perturbed Systems Subject to Markov Jump Parameters
Wang J., Yang C., Shen H., Cao J., Rutkowski L., 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
Distributed Dynamic Self-Triggered Impulsive Control for Consensus Networks: The Case of Impulse Gain with Normal Distribution
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, IEEE Transactions on Cybernetics, 51, 51, 624-634, 2021, Cites: 31
Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control with Actuator Fault
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, IEEE Transactions on Neural Networks and Learning Systems, 32, 32, 4191-4201, 2021, Cites: 66
Optimal Control on Finite-Time Consensus of the Leader-Following Stochastic Multiagent System with Heuristic Method
Xu S., Cao J., Liu Q., Rutkowski L., 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
A diversified shared latent variable model for efficient image characteristics extraction and modelling
Xiong H., Tang Y.Y., Murtagh F., Rutkowski L., Berkovsky S., A diversified shared latent variable model for efficient image characteristics extraction and modelling, Neurocomputing, 421, 421, 244-259, 2021, Cites: 2

2020 (30)

Hybrid Splitting Criteria
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
Regression
Rutkowski L., Jaworski M., Duda P., Regression, Studies in Big Data, 56, 56, 309-322, 2020, Cites: 0
Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks
Rutkowski L., Jaworski M., Duda P., Nonparametric Regression Models for Data Streams Based on the Generalized Regression Neural Networks, Studies in Big Data, 56, 56, 173-244, 2020, Cites: 1
Splitting Criteria with the Bias Term
Rutkowski L., Jaworski M., Duda P., Splitting Criteria with the Bias Term, Studies in Big Data, 56, 56, 83-89, 2020, Cites: 0
Probabilistic Neural Networks for the Streaming Data Classification
Rutkowski L., Jaworski M., Duda P., Probabilistic Neural Networks for the Streaming Data Classification, Studies in Big Data, 56, 56, 245-277, 2020, Cites: 2
Introduction and Overview of the Main Results of the Book
Rutkowski L., Jaworski M., Duda P., Introduction and Overview of the Main Results of the Book, Studies in Big Data, 56, 56, 1-10, 2020, Cites: 1
Explainable Cluster-Based Rules Generation for Image Retrieval and Classification
Staszewski P., Jaworski M., Rutkowski L., Tao D., 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
Fully Convolutional Network for Removing DCT Artefacts from Images
Najgebauer P., Scherer R., Rutkowski L., Fully Convolutional Network for Removing DCT Artefacts from Images, Proceedings of the International Joint Conference on Neural Networks, 2020, Cites: 4
Misclassification Error Impurity Measure
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 1
Lyapunov Functions for the Set Stability and the Synchronization of Boolean Control Networks
Chen B., Cao J., Lu G., Rutkowski L., 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
Splitting Criteria Based on the McDiarmid’s Theorem
Rutkowski L., Jaworski M., Duda P., Splitting Criteria Based on the McDiarmid’s Theorem, Studies in Big Data, 56, 56, 51-62, 2020, Cites: 0
Robust Event-Triggered Control Invariance of Probabilistic Boolean Control Networks
Lin L., Cao J., Rutkowski L., 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
Complex systems and networks with their applications
Cao J.-D., Liu Y., Lu J.-Q., Rutkowski L., Complex systems and networks with their applications, Frontiers of Information Technology and Electronic Engineering, 21, 21, 195-198, 2020, Cites: 1
Decision Trees in Data Stream Mining
Rutkowski L., Jaworski M., Duda P., Decision Trees in Data Stream Mining, Studies in Big Data, 56, 56, 37-50, 2020, Cites: 7
Final Remarks and Challenging Problems
Rutkowski L., Jaworski M., Duda P., Final Remarks and Challenging Problems, Studies in Big Data, 56, 56, 323-327, 2020, Cites: 0
Anti-synchronization in fixed time for discontinuous reaction-diffusion neural networks with time-varying coefficients and time delay
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, IEEE Transactions on Cybernetics, 50, 50, 2758-2769, 2020, Cites: 60
Classification
Rutkowski L., Jaworski M., Duda P., Classification, Studies in Big Data, 56, 56, 287-308, 2020, Cites: 0
Synchronization of Coupled Time-Delay Neural Networks with Mode-Dependent Average Dwell Time Switching
Yang X., Liu Y., Cao J., Rutkowski L., 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
Event-triggered impulsive synchronization of discrete-time coupled neural networks with stochastic perturbations and multiple delays
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, Neural Networks, 132, 132, 447-460, 2020, Cites: 17
Sampled-Data Set Stabilization of Impulsive Boolean Networks Based on a Hybrid Index Model
Lin L., Cao J., Zhu S., Rutkowski L., Lu G., 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
Concept Drift Detection Using Autoencoders in Data Streams Processing
Jaworski M., Rutkowski L., Angelov P., 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
General Non-parametric Learning Procedure for Tracking Concept Drift
Rutkowski L., Jaworski M., Duda P., General Non-parametric Learning Procedure for Tracking Concept Drift, Studies in Big Data, 56, 56, 155-172, 2020, Cites: 1
The General Procedure of Ensembles Construction in Data Stream Scenarios
Rutkowski L., Jaworski M., Duda P., The General Procedure of Ensembles Construction in Data Stream Scenarios, Studies in Big Data, 56, 56, 281-286, 2020, Cites: 0
Constrained Quaternion-Variable Convex Optimization: A Quaternion-Valued Recurrent Neural Network Approach
Liu Y., Zheng Y., Lu J., Cao J., Rutkowski L., 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
Basic Concepts of Probabilistic Neural Networks
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Probabilistic Neural Networks, Studies in Big Data, 56, 56, 117-154, 2020, Cites: 0
A Novel Explainable Recommender for Investment Managers
Rutkowski T., Nielek R., Rutkowska D., Rutkowski L., 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
Asymptotic Output Tracking of Probabilistic Boolean Control Networks
Chen B., Cao J., Luo Y., Rutkowski L., Asymptotic Output Tracking of Probabilistic Boolean Control Networks, IEEE Transactions on Circuits and Systems I: Regular Papers, 67, 67, 2780-2790, 2020, Cites: 24
On the Parzen Kernel-Based Probability Density Function Learning Procedures over Time-Varying Streaming Data with Applications to Pattern Classification
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, IEEE Transactions on Cybernetics, 50, 50, 1683-1696, 2020, Cites: 26
Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks with Markov Switching Topologies and Random Time-Varying Delay
Tan X., Cao J., Rutkowski L., 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
Basic Concepts of Data Stream Mining
Rutkowski L., Jaworski M., Duda P., Basic Concepts of Data Stream Mining, Studies in Big Data, 56, 56, 13-33, 2020, Cites: 17

2019 (7)

Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection
Najgebauer P., Grycuk R., Rutkowski L., Scherer R., Siwocha A., 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
On the hermite series-based generalized regression neural networks for stream data mining
Rutkowska D., Rutkowski L., 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
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))
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)), Information Sciences, 477, 477, 545, 2019, Cites: 0
Resource-aware data stream mining using the restricted boltzmann machine
Jaworski M., Rutkowski L., Duda P., Cader A., 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
Exponential Stabilization for Hybrid Recurrent Neural Networks by Delayed Noises Rooted in Discrete Observations of State and Mode
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, Neural Processing Letters, 50, 50, 2797-2819, 2019, Cites: 2
On Proper Designing of Deep Structures for Image Classification
Woldan P., Staszewski P., Rutkowski L., Grzanek K., 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
On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine
Jaworski M., Duda P., Rutkowska D., Rutkowski L., 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: 1

2018 (11)

Concept Drift Detection in Streams of Labelled Data Using the Restricted Boltzmann Machine
Jaworski M., Duda P., Rutkowski L., 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
Preface
Rutkowski L., 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
Online grnn-based ensembles for regression on evolving data streams
Duda P., Jaworski M., Rutkowski L., 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
On the global convergence of the parzen-based generalized regression neural networks applied to streaming data
Cao J., Rutkowski L., 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
New Splitting Criteria for Decision Trees in Stationary Data Streams
Jaworski M., Duda P., Rutkowski L., 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
Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks
Duda P., Jaworski M., Rutkowski L., 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
A content-based recommendation system using neuro-fuzzy approach
Rutkowski T., Romanowski J., Woldan P., Staszewski P., Nielek R., Rutkowski L., A content-based recommendation system using neuro-fuzzy approach, IEEE International Conference on Fuzzy Systems, 2018-July, 2018-July, 2018, Cites: 43
Fuzzy-genetic approach to identity verification using a handwritten signature
Zalasinski M., Cpalka K., Rutkowski L., Fuzzy-genetic approach to identity verification using a handwritten signature, Studies in Computational Intelligence, 738, 738, 375-394, 2018, Cites: 6
Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks
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, International Journal of Neural Systems, 28, 28, 2018, Cites: 30
New aspects of interpretability of fuzzy systems for nonlinear modeling
Lapa K., Cpalka K., Rutkowski L., New aspects of interpretability of fuzzy systems for nonlinear modeling, Studies in Computational Intelligence, 738, 738, 225-264, 2018, Cites: 14
Preface
Rutkowski L., 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: 0

2017 (6)

How to adjust an ensemble size in stream data mining?
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., How to adjust an ensemble size in stream data mining?, Information Sciences, 381, 381, 46-54, 2017, Cites: 60
Preface
Rutkowski L., 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
Heuristic regression function estimation methods for data streams with concept drift
Jaworski M., Duda P., Rutkowski L., Najgebauer P., Pawlak M., 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
Novel method for joining missing line fragments for medical image analysis
Najgebauer P., Rutkowski L., Scherer R., 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
Interest point localization based on edge detection according to gestalt laws
Najgebauer P., Rutkowski L., Scherer R., 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
Preface
Rutkowski L., 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: 0

2016 (8)

A method for automatic adjustment of ensemble size in stream data mining
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., 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
Preface
Rutkowski L., 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
Novel rough neural network for classification with missing data
Nowicki R.K., Scherer R., Rutkowski L., 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
A new algorithm for identity verification based on the analysis of a handwritten dynamic signature
Cpalka K., Zalasinski M., Rutkowski L., 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
Fast image classification by boosting fuzzy classifiers
Korytkowski M., Rutkowski L., Scherer R., Fast image classification by boosting fuzzy classifiers, Information Sciences, 327, 327, 175-182, 2016, Cites: 136
Hybrid splitting criterion in decision trees for data stream mining
Jaworski M., Rutkowski L., Pawlak M., 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
Artificial intelligence and soft computing: 15th international conference, ICAISC 2016 Zakopane, Poland, June 12-16, 2016 proceedings, Part I
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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 2016, Cites: 0
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, V-VI, 2016, Cites: 0

2015 (5)

Preface
Rutkowski L., Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, V-VII, 2015, Cites: 0
A new method for data stream mining based on the misclassification error
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., 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
Preface
Rutkowski L., Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, V-VII, 2015, Cites: 0
Customization of joint articulations using soft computing methods
Szarek A., Korytkowski M., Rutkowski L., Scherer M., Szyprowski J., Kostadinov D., 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
Fast dictionary matching for content-based image retrieval
Najgebauer P., Rygal J., Nowak T., Romanowski J., Rutkowski L., Voloshynovskiy S., Scherer R., 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: 2

2014 (5)

New method for the on-line signature verification based on horizontal partitioning
Cpalka K., Zalasinski M., Rutkowski L., New method for the on-line signature verification based on horizontal partitioning, Pattern Recognition, 47, 47, 2652-2661, 2014, Cites: 81
The CART decision tree for mining data streams
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., The CART decision tree for mining data streams, Information Sciences, 266, 266, 1-15, 2014, Cites: 241
Decision trees for mining data streams based on the gaussian approximation
Rutkowski L., Jaworski M., Pietruczuk L., Duda P., 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
A novel application of Hoeffding's inequality to decision trees construction for data streams
Duda P., Jaworski M., Pietruczuk L., Rutkowski L., 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
The Parzen kernel approach to learning in non-stationary environment
Pietruczuk L., Rutkowski L., Jaworski M., Duda P., The Parzen kernel approach to learning in non-stationary environment, Proceedings of the International Joint Conference on Neural Networks, 3319-3323, 2014, Cites: 11

2013 (3)

On design of flexible neuro-fuzzy systems for nonlinear modelling
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., On design of flexible neuro-fuzzy systems for nonlinear modelling, International Journal of General Systems, 42, 42, 706-720, 2013, Cites: 66
Object detection by simple fuzzy classifiers generated by boosting
Gabryel M., Korytkowski M., Scherer R., Rutkowski L., 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
Decision trees for mining data streams based on the mcdiarmid's bound
Rutkowski L., Pietruczuk L., Duda P., Jaworski M., 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: 161

2012 (6)

Forecasting wear of head and acetabulum in hip joint implant
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., 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
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
Rutkowski L., 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
Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation
Rutkowski L., Przybyl A., Cpalka K., 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
Neuro-fuzzy systems
Rutkowski L., Cpalka K., Nowicki R., Pokropinska A., Scherer R., Neuro-fuzzy systems, Computational Complexity: Theory, Techniques, and Applications, 9781461418009, 9781461418009, 2069-2081, 2012, Cites: 4
Preface
Rutkowski L., 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
Application of neural networks in assessing changes around implant after total hip arthroplasty
Szarek A., Korytkowski M., Rutkowski L., Scherer R., Szyprowski J., 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: 23

2011 (4)

AdaBoost ensemble of DCOG rough-neuro-fuzzy systems
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., 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
Foreword
Rutkowski L., Foreword, Intelligent Systems Reference Library, 6, 6, 2011, Cites: 0
Rule base normalization in Takagi-Sugeno ensemble
Korytkowski M., Rutkowski L., Scherer R., 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
On designing of flexible neuro-fuzzy systems for nonlinear modelling
Cpalka K., Rebrova O., Nowicki R., Rutkowski L., 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: 2

2010 (9)

Evolutionary designing of logic-type fuzzy systems
Gabryel M., Rutkowski L., 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
MICOG defuzzification rough-neuro-fuzzy system ensemble
Korytkowski M., Nowicki R.K., Scherer R., Rutkowski L., MICOG defuzzification rough-neuro-fuzzy system ensemble, 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010, Cites: 5
Fault diagnosis of an air-handling unit system using a dynamic fuzzy-neural approach
Du J., Er M.J., Rutkowski L., 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
Neural network-based assessment of femur stress after hip joint alloplasty
Korytkowski M., Rutkowski L., Scherer R., Szarek A., 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
Fuzzy regression modeling for tool performance prediction and degradation detection
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, International Journal of Neural Systems, 20, 20, 405-419, 2010, Cites: 64
On automatic design of neuro-fuzzy systems
Cpalka K., Rutkowski L., Er M.J., 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
An online approach towards self-generating fuzzy neural networks with applications
Liu F., Er M.J., Rutkowski L., An online approach towards self-generating fuzzy neural networks with applications, Proceedings of the International Joint Conference on Neural Networks, 2010, Cites: 0
Online speed profile generation for industrial machine tool based on neuro-fuzzy approach
Rutkowski L., Przybyl A., Cpalka K., Er M.J., 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
An Efficient Adaptive Fuzzy Neural Network (EAFNN) approach for short term load forecasting
Du J., Er M.J., Rutkowski L., 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: 3

2009 (1)

A new method for complexity reduction of neuro-fuzzy systems with application to differential stroke diagnosis
Cpalka K., Rebrova O., Rutkowski L., 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: 2

2008 (10)

From ensemble of fuzzy classifiers to single fuzzy rule base classifier
Korytkowski M., Rutkowski L., Scherer R., 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
Lecture Notes in Artificial Intelligence : Preface
Rutkowski L., 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
An application of weighted triangular norms to complexity reduction of neuro-fuzzy systems
Cpalka K., Rutkowski L., 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
Computational intelligence: Methods and techniques
Rutkowski L., Computational intelligence: Methods and techniques, Computational Intelligence: Methods and Techniques, 1-514, 2008, Cites: 301
Evolutionary learning of flexible neuro-fuzzy systems
Cpalka K., Rutkowski L., Evolutionary learning of flexible neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 969-975, 2008, Cites: 10
Evolutionary methods to create interpretable modular system
Korytkowski M., Gabryel M., Rutkowski L., Drozda S., 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
On differential stroke diagnosis by neuro-fuzzy structures
Cpalka K., Rebrova O., Galkowski T., Rutkowski L., 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
Ensemble of rough-neuro-fuzzy systems for classification with missing features
Korytkowski M., Nowicki R., Scherer R., Rutkowski L., Ensemble of rough-neuro-fuzzy systems for classification with missing features, IEEE International Conference on Fuzzy Systems, 1745-1750, 2008, Cites: 13
Evolutionary methods for designing neuro-fuzzy modular systems combined by bagging algorithm
Gabryel M., Rutkowski L., 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
Modular rough neuro-fuzzy systems for classification
Scherer R., Korytkowski M., Nowicki R., Rutkowski L., 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: 3

2007 (3)

On speeding up the learning process of neuro-fuzzy ensembles generated by the adaboost algorithm
Korytkowski M., Rutkowski L., Scherer R., 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
On obtaining fuzzy rule base from ensemble of Takagi-Sugeno systems
Korytkowski M., Rutkowski L., Scherer R., Drozda G., 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
Rough-neuro-fuzzy systems for classification
Cpalka K., Nowicki R., Rutkowski L., Rough-neuro-fuzzy systems for classification, Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 1-8, 2007, Cites: 3

2006 (6)

A new method for designing and reduction of neuro-fuzzy systems
Cpalka K., Rutkowski L., A new method for designing and reduction of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1851-1857, 2006, Cites: 16
Evolutionary learning of mamdani-type neuro-fuzzy systems
Gabryel M., Rutkowski L., 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
Merging ensemble of neuro-fuzzy systems
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., Merging ensemble of neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 1954-1957, 2006, Cites: 7
On combining backpropagation with boosting
Korytkowski M., Rutkowski L., Scherer R., On combining backpropagation with boosting, IEEE International Conference on Neural Networks - Conference Proceedings, 1274-1277, 2006, Cites: 58
A new method for complexity reduction of neuro-fuzzy systems
Cpalka K., Rutkowski L., A new method for complexity reduction of neuro-fuzzy systems, WSEAS Transactions on Systems, 5, 5, 2514-2521, 2006, Cites: 1
Combining logical-type neuro-fuzzy systems
Korytkowski M., Nowicki R., Rutkowski L., Scherer R., 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: 1

2005 (4)

Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation, WSEAS Transactions on Systems, 4, 4, 1450-1458, 2005, Cites: 42
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
Rutkowski L., Cpalka K., 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
Flexible Takagi-Sugeno fuzzy systems
Cpalka K., Rutkowski L., Flexible Takagi-Sugeno fuzzy systems, Proceedings of the International Joint Conference on Neural Networks, 3, 3, 1764-1769, 2005, Cites: 56
Flexible neuro-fuzzy structures for pattern classification
Cpalka K., Rutkowski L., Flexible neuro-fuzzy structures for pattern classification, WSEAS Transactions on Computers, 4, 4, 679-688, 2005, Cites: 7

2004 (6)

Neuro-fuzzy relational classifiers
Scherer R., Rutkowski L., Neuro-fuzzy relational classifiers, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 3070, 3070, 376-380, 2004, Cites: 23
Adaptive probabilistic neural networks for pattern classification in time-varying environment
Rutkowski L., Adaptive probabilistic neural networks for pattern classification in time-varying environment, IEEE Transactions on Neural Networks, 15, 15, 811-827, 2004, Cites: 196
Neuro-fuzzy systems derived from quasi-triangular norms
Rutkowski L., Cpalka K., Neuro-fuzzy systems derived from quasi-triangular norms, IEEE International Conference on Fuzzy Systems, 2, 2, 1031-1036, 2004, Cites: 39
Fuzzy modelling with a compromise fuzzy reasoning
Cpalka K., Rutkowski L., 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
A new method for system modelling and pattern classification
Rutkowski L., 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
Generalized regression neural networks in time-varying environment
Rutkowski L., Generalized regression neural networks in time-varying environment, IEEE Transactions on Neural Networks, 15, 15, 576-596, 2004, Cites: 91

2003 (4)

A Hierarchical Neuro-Fuzzy System Based on S-Implications
Nowicki R., Scherer R., Rutkowski L., 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
Flexible neuro-fuzzy systems
Rutkowski L., Cpalka K., Flexible neuro-fuzzy systems, IEEE Transactions on Neural Networks, 14, 14, 554-574, 2003, Cites: 180
Erratum: Flexible neuro-fuzzy systems (IEEE Transactions on Neural Networks (May 2003) 14 (554-574))
Rutkowski L., Cpalka K., 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
A new approach to designing fuzzy systems
Rutkowski L., Cpalka K., A new approach to designing fuzzy systems, Recent Advances in Intelligent Systems and Signal Processing, 343-347, 2003, Cites: 0

2002 (3)

Flexible weighted neuro-fuzzy systems
Rutkowski L., Cpalka K., 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
Connectionist structures of type 2 fuzzy inference systems
Starczewski J., Rutkowski L., 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
A neuro - Fuzzy controller with a compromise fuzzy reasoning
Rutkowski L., Cpalka K., A neuro - Fuzzy controller with a compromise fuzzy reasoning, Control and Cybernetics, 31, 31, 297-308, 2002, Cites: 56

2001 (2)

Neuro-fuzzy systems with inference based on bounded product
Rutkowska D., Rutkowski L., Nowicki R., Neuro-fuzzy systems with inference based on bounded product, Advances in Neural Networks and Applications, 104-109, 2001, Cites: 5
A general approach to neuro-fuzzy systems
Rutkowski L., Cpalka K., A general approach to neuro-fuzzy systems, IEEE International Conference on Fuzzy Systems, 3, 3, 1428-1431, 2001, Cites: 47

2000 (1)

On image compression by competitive neural networks and optimal linear predictors
Cierniak R., Rutkowski L., On image compression by competitive neural networks and optimal linear predictors, Signal Processing: Image Communication, 15, 15, 559-565, 2000, Cites: 51

1998 (1)

A fast training algorithm for neural networks
Bilski J., Rutkowski L., 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: 64

1996 (1)

Neural networks and semi-closed-loop predictive vector quantization for image compression
Cierniak Robert, Rutkowski Leszek, Neural networks and semi-closed-loop predictive vector quantization for image compression, IEEE International Conference on Image Processing, 1, 1, 245-248, 1996, Cites: 1

1993 (1)

Multiple Fourier Series Procedures for Extraction of Nonlinear Regressions from Noisy Data
Rutkowski L., Multiple Fourier Series Procedures for Extraction of Nonlinear Regressions from Noisy Data, IEEE Transactions on Signal Processing, 41, 41, 3062-3065, 1993, Cites: 52

1991 (1)

Identification of MISO Nonlinear Regressions in the Presence of a Wide Class of Disturbances
Rutkowski L., 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: 53

1989 (5)

Application of multiple fourier series to identification of multivariable non-stationary systems
Rutkowski L., Application of multiple fourier series to identification of multivariable non-stationary systems, International Journal of Systems Science, 20, 20, 1993-2002, 1989, Cites: 54
Nonparametric procedure for identification of the step response function and its microprocessor implementation
Kozietulski Marek, Rutkowski Leszek, Nonparametric procedure for identification of the step response function and its microprocessor implementation, Advances in modelling &amp; simulation, 17, 17, 25-36, 1989, Cites: 1
Non-parametric learning algorithms in time-varying environments
Rutkowski L., Non-parametric learning algorithms in time-varying environments, Signal Processing, 18, 18, 129-137, 1989, Cites: 50
Nonparametric identification of input signals in distributed systems
Rafajlowicz Ewaryst, Rutkowski Leszek, Nonparametric identification of input signals in distributed systems, Proceedings of the IEEE Conference on Decision and Control, 2, 2, 1464-1465, 1989, Cites: 0
On Optimal Global Rate of Convergence of Some Nonparametric Identification Procedures
Rutkowski L., Rafajlowicz E., On Optimal Global Rate of Convergence of Some Nonparametric Identification Procedures, IEEE Transactions on Automatic Control, 34, 34, 1089-1091, 1989, Cites: 59

1988 (1)

Sequential pattern recognition procedures derived from multiple Fourier series
Rutkowski L., Sequential pattern recognition procedures derived from multiple Fourier series, Pattern Recognition Letters, 8, 8, 213-216, 1988, Cites: 52

Schedule