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Contact:
Room: 523
Position:
Honorary Professor
Prof. PhD DSc Eng Leszek Rutkowski

Papers (200)

2024 (28)

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, 20, 20, 4218-4226, 2024, Cites: 2
Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems With Stochastic Communication Protocol
Wu J., Cheng J., Yan H., Rutkowski L., Cao J., Observer-Based Sliding Mode Control for Stochastic Sampling Fuzzy Systems With Stochastic Communication Protocol, IEEE Transactions on Fuzzy Systems, 2024, Cites: 0
Space-Time Sampled-Data Control for Memristor-Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities
Cheng J., Liu N., Rutkowski L., Cao J., Yan H., Hua L., Space-Time Sampled-Data Control for Memristor-Based Reaction-Diffusion Neural Networks With Nonhomogeneous Sojourn Probabilities, IEEE Transactions on Circuits and Systems I: Regular Papers, 2024, Cites: 0
Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus
Ju Y., Xiao M., Huang C., Rutkowski L., Cao J., Hybrid control of Turing instability and bifurcation for spatial-temporal propagation of computer virus, International Journal of Systems Science, 55, 55, 2187-2210, 2024, Cites: 0
Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games
Chen G., Xu G., He F., Hong Y., Rutkowski L., Tao D., Approaching the Global Nash Equilibrium of Non-Convex Multi-Player Games, IEEE Transactions on Pattern Analysis and Machine Intelligence, 46, 46, 10797-10813, 2024, Cites: 0
Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks
He J., Xiao M., He H., Wang Z., Xing Zheng W., Rutkowski L., Facilitating and Determining Turing Patterns in 3-D Memristor Cellular Neural Networks, IEEE Transactions on Circuits and Systems I: Regular Papers, 71, 71, 4131-4144, 2024, Cites: 0
Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy
Lin L., Cao J., Lu J., Rutkowski L., Set Stabilization of Large-Scale Stochastic Boolean Networks: A Distributed Control Strategy, IEEE/CAA Journal of Automatica Sinica, 11, 11, 806-808, 2024, Cites: 4
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: 3
(μ +λ) Evolution Strategy with Socio-Cognitive Mutation
Urbanczyk A., Kucaba K., Wojtulewicz M., Kisiel-Dorohinicki M., Rutkowski L., Duda P., Kacprzyk J., Yao X., Chong S.Y., Byrski A., (μ +λ) Evolution Strategy with Socio-Cognitive Mutation, Journal of Automation, Mobile Robotics and Intelligent Systems, 18, 18, 1-11, 2024, Cites: 0
Task offloading strategies for mobile edge computing: A survey
Dong S., Tang J., Abbas K., Hou R., Kamruzzaman J., Rutkowski L., Buyya R., Task offloading strategies for mobile edge computing: A survey, Computer Networks, 254, 254, 2024, Cites: 0
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, 71, 71, 1316-1320, 2024, Cites: 2
Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines
Vovna O., Kaydash H., Rutkowski L., Sakhno I., Laktionov I., Kabanets M., Zozulya S., Computer-Integrated Monitoring Technology with Support-Decision of Unauthorized Disturbance of Methane Sensor Functioning for Coal Mines, Journal of Control Science and Engineering, 2024, 2024, 2024, Cites: 0
Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations
Ademola A.T., Wen S., Feng Y., Zhang W., Rutkowski L., Stability and boundedness criteria for certain second-order nonlinear neutral stochastic functional differential equations, Proyecciones, 43, 43, 985-1009, 2024, Cites: 0
A new chemical networked system: spatial-temporal evolution and control
Li H., Xiao M., Wang Z., Xu F., Wang Z., Zheng W., Rutkowski L., A new chemical networked system: spatial-temporal evolution and control, Physica Scripta, 99, 99, 2024, Cites: 0
How to regulate pattern formations for malware propagation in cyber-physical systems
Cheng H., Xiao M., Yu W., Rutkowski L., Cao J., How to regulate pattern formations for malware propagation in cyber-physical systems, Chaos, 34, 34, 2024, Cites: 1
Complex pattern evolution of a two-dimensional space diffusion model of malware spread
Cheng H., Xiao M., Lu Y., Bao H., Rutkowski L., Cao J., Complex pattern evolution of a two-dimensional space diffusion model of malware spread, Physica Scripta, 99, 99, 2024, Cites: 0
Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks
Du X., Xiao M., Luan Y., Ding J., Rutkowski L., Full-Dimensional Proportional-Derivative Control Technique for Turing Pattern and Bifurcation of Delayed Reaction-Diffusion Bidirectional Ring Neural Networks, Journal of Computational and Nonlinear Dynamics, 19, 19, 2024, Cites: 0
Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies
Lv X., Cao J., Rutkowski L., Duan P., Distributed Saturated Impulsive Control for Local Consensus of Nonlinear Time-Delay Multiagent Systems with Switching Topologies, IEEE Transactions on Automatic Control, 69, 69, 771-782, 2024, Cites: 4
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, 35, 35, 1394-1400, 2024, Cites: 6
Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control
Kong F., Cao J., Rutkowski L., Zhang Y., Finite-Time Control of Fuzzy Competitive Networks via Comparison Method and Bounded Control, IEEE Transactions on Fuzzy Systems, 32, 32, 3059-3070, 2024, Cites: 2
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
A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains
Lin L., Cao J., Lam J., Rutkowski L., Dimirovski G.M., Zhu S., A Bisimulation-Based Foundation for Scale Reductions of Continuous-Time Markov Chains, IEEE Transactions on Automatic Control, 69, 69, 5743-5758, 2024, Cites: 6
Ant colony optimization using two-dimensional pheromone for single-objective transport problems
Starzec G., Starzec M., Rutkowski L., Kisiel-Dorohinicki M., Byrski A., Ant colony optimization using two-dimensional pheromone for single-objective transport problems, Journal of Computational Science, 79, 79, 2024, Cites: 1
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, 23, 23, 2566-2582, 2024, Cites: 5
Accelerating deep neural network learning using data stream methodology
Duda P., Wojtulewicz M., Rutkowski L., Accelerating deep neural network learning using data stream methodology, Information Sciences, 669, 669, 2024, Cites: 1
Leader-Follower Consensus Over Finite Fields
Lin L., Cao J., Lam J., Zhu S., Azuma S.-I., Rutkowski L., Leader-Follower Consensus Over Finite Fields, IEEE Transactions on Automatic Control, 69, 69, 4718-4725, 2024, Cites: 7
Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks
Huang X.-X., Xiao M., Rutkowski L., Bao H.-B., Huang X., Cao J.-D., Mechanism analysis of regulating Turing instability and Hopf bifurcation of malware propagation in mobile wireless sensor networks, Chinese Physics B, 33, 33, 2024, Cites: 0
Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring
Rutkowska D., Duda P., Cao J., Jaworski M., Kisiel-Dorohinicki M., Tao D., Rutkowski L., Probabilistic neural networks for incremental learning over time-varying streaming data with application to air pollution monitoring, Applied Soft Computing, 161, 161, 2024, Cites: 0

2023 (27)

On Computing Paradigms - Where Will Large Language Models Be Going
Wu X., Zhu X., Baralis E., Lu R., Kumar V., Rutkowski L., Tang J., On Computing Paradigms - Where Will Large Language Models Be Going, Proceedings - IEEE International Conference on Data Mining, ICDM, 1577-1582, 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: 5
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: 12
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, 68, 68, 7299-7311, 2023, Cites: 13
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: 20
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
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: 1
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, v-vi, 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: 13
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: 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: 1
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
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
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: 6
Fuzzy H<inf>∞</inf> 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 H<inf>∞</inf> Control of Semi-Markov Jump Singularly Perturbed Nonlinear Systems With Partial Information and Actuator Saturation, IEEE Transactions on Fuzzy Systems, 31, 31, 4374-4384, 2023, Cites: 4
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: 15
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: 4
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, 25, 25, 8279-8293, 2023, Cites: 0
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14126 LNAI, 14126 LNAI, v-vi, 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: 3
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, v-vi, 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: 41
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13588 LNAI, 13588 LNAI, v-vi, 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: 6
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
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
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: 6

2022 (16)

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: 10
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 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: 43
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: 46
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: 15
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: 7
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: 13
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: 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: 9
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: 6
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: 30
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: 20
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: 16
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: 19
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: 20
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: 62

2021 (13)

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: 98
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: 21
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: 2
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: 16
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: 43
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, v-vi, 2021, Cites: 0
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, v-vi, 2021, Cites: 0
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: 5
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: 118
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: 47
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: 16
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

2020 (32)

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
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: 73
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: 2
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: 21
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
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
Classification
Rutkowski L., Jaworski M., Duda P., Classification, Studies in Big Data, 56, 56, 287-308, 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: 96
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
Regression
Rutkowski L., Jaworski M., Duda P., Regression, Studies in Big Data, 56, 56, 309-322, 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: 34
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: 35
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
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: 44
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
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: 18
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, v-vi, 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: 75
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
Misclassification Error Impurity Measure
Rutkowski L., Jaworski M., Duda P., Misclassification Error Impurity Measure, Studies in Big Data, 56, 56, 63-82, 2020, Cites: 2
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: 14
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12416 LNAI, 12416 LNAI, v-vi, 2020, Cites: 0
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: 32
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: 6
Hybrid Splitting Criteria
Rutkowski L., Jaworski M., Duda P., Hybrid Splitting Criteria, Studies in Big Data, 56, 56, 91-113, 2020, Cites: 1
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: 112
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
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
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
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
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: 23

2019 (9)

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
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
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: 2
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11509 LNAI, 11509 LNAI, v-vi, 2019, Cites: 0
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
Preface
Rutkowski L., Preface, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, v-vi, 2019, Cites: 0
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
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: 6

2018 (11)

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: 16
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
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
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: 13
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
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: 50
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: 31
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: 89
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
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

2017 (8)

On applying the Restricted Boltzmann Machine to active concept drift detection
Jaworski M., Duda P., Rutkowski L., On applying the Restricted Boltzmann Machine to active concept drift detection, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-8, 2017, Cites: 22
On ensemble components selection in data streams scenario with reoccurring concept-drift
Duda P., Jaworski M., Rutkowski L., On ensemble components selection in data streams scenario with reoccurring concept-drift, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018-January, 2018-January, 1-7, 2017, Cites: 16
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: 65
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
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), 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

2016 (8)

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
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
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
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: 7
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: 141
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: 18
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: 91
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

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
Preface
Rutkowski L., Preface, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, V-VII, 2015, Cites: 0
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
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
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: 104

2014 (6)

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: 270
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: 12
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: 86
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: 144
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), 8467 LNAI, 8467 LNAI, 2014, Cites: 1
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: 16

2013 (3)

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
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: 67
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: 168

2012 (6)

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
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: 2
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
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
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: 90
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

2011 (4)

Foreword
Rutkowski L., Foreword, Intelligent Systems Reference Library, 6, 6, 2011, Cites: 0
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
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
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

2010 (10)

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: 54
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
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), 6114 LNAI, 6114 LNAI, 2010, Cites: 5
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
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
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
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
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
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: 66
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)

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
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
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
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: 14
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
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: 62
Computational intelligence: Methods and techniques
Rutkowski L., Computational intelligence: Methods and techniques, Computational Intelligence: Methods and Techniques, 1-514, 2008, Cites: 306
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

2007 (3)

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
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
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

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