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Contact:
Room: 520
Position:
Full Professor
Classes:
Programowanie obiektowe lab
Programowanie obiektowe wyk
Prof. PhD DSc Eng Krzysztof Cpałka
Office hours: poniedziałek 8:15-12:00
Więcej informacji można uzyskać na stronie: https://kisi.pcz.pl/krzysztofcpalka.

Papers (113)

2023 (4)

Evolutionary Algorithms and Their Applications in Intelligent Systems
Slowik A., Cpalka K., Hassanien A.E., Evolutionary Algorithms and Their Applications in Intelligent Systems, Lecture Notes on Data Engineering and Communications Technologies, 184, 184, 143-153, 2023, Cites: 0
AN INTELLIGENT APPROACH TO SHORT-TERM WIND POWER PREDICTION USING DEEP NEURAL NETWORKS
Niksa-Rynkiewicz T., Stomma P., Witkowska A., Rutkowska D., Slowik A., Cpalka K., Jaworek-Korjakowska J., Kolendo P., AN INTELLIGENT APPROACH TO SHORT-TERM WIND POWER PREDICTION USING DEEP NEURAL NETWORKS, Journal of Artificial Intelligence and Soft Computing Research, 13, 13, 197-210, 2023, Cites: 0
Dynamic Signature Verification Using Selected Regions
Zalasinski M., Duda P., Lota S., Cpalka K., Dynamic Signature Verification Using Selected Regions, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13589 LNAI, 13589 LNAI, 388-397, 2023, Cites: 1
Multi-population Algorithm Using Surrogate Models and Different Training Plans
Kucharski D., Cpalka K., Multi-population Algorithm Using Surrogate Models and Different Training Plans, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14125 LNAI, 14125 LNAI, 385-398, 2023, Cites: 0

2022 (5)

Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation
Lapa K., Cpalka K., Kisiel-Dorohinicki M., Paszkowski J., Debski M., Le V.-H., Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 239-253, 2022, Cites: 3
Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach
Zalasinski M., Laskowski L., Niksa-Rynkiewicz T., Cpalka K., Byrski A., Przybyszewski K., Trippner P., Dong S., Evolutionary Algorithm for Selecting Dynamic Signatures Partitioning Approach, Journal of Artificial Intelligence and Soft Computing Research, 12, 12, 267-279, 2022, Cites: 5
Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications
Slowik A., Cpalka K., Guest Editorial: Hybrid Approaches to Nature-Inspired Population-Based Intelligent Optimization for Industrial Applications, IEEE Transactions on Industrial Informatics, 18, 18, 542-545, 2022, Cites: 4
Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications
Slowik A., Cpalka K., Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications, IEEE Transactions on Industrial Informatics, 18, 18, 546-558, 2022, Cites: 11
A population-based algorithm with the selection of evaluation precision and size of the population
Cpalka K., Slowik A., Lapa K., A population-based algorithm with the selection of evaluation precision and size of the population, Applied Soft Computing, 115, 115, 2022, Cites: 2

2021 (5)

Synthesis of vertically aligned porous silica thin films functionalized by silver ions
Fedorchuk A., Walcarius A., Laskowska M., Vila N., Kowalczyk P., Cpalka K., Laskowski L., Synthesis of vertically aligned porous silica thin films functionalized by silver ions, International Journal of Molecular Sciences, 22, 22, 2021, Cites: 4
Population Management Approaches in the OPn Algorithm
Lapa K., Cpalka K., Slowik A., Population Management Approaches in the OPn Algorithm, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12854 LNAI, 12854 LNAI, 402-414, 2021, Cites: 1
Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network
Niksa-Rynkiewicz T., Szewczuk-Krypa N., Witkowska A., Cpalka K., Zalasinski M., Cader A., Monitoring regenerative heat exchanger in steam power plant by making use of the recurrent neural network, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 143-155, 2021, Cites: 11
A low-dimensional layout of magnetic units as nano-systems of combinatorial logic: Numerical simulations
Kuzma D., Kowalczyk P., Cpalka K., Laskowski L., A low-dimensional layout of magnetic units as nano-systems of combinatorial logic: Numerical simulations, Materials, 14, 14, 2021, Cites: 1
Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms
Zalasinski M., Niksa-Rynkiewicz T., Cpalka K., Dynamic Signature Vertical Partitioning Using Selected Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12855 LNAI, 12855 LNAI, 511-518, 2021, Cites: 0

2020 (11)

Nanocomposite for photonics — Nickel pyrophosphate nanocrystals synthesised in silica nanoreactors
Laskowska M., Kityk I., Pastukh O., Dulski M., Zubko M., Jedryka J., Cpalka K., Zielinski P.M., Laskowski, Nanocomposite for photonics — Nickel pyrophosphate nanocrystals synthesised in silica nanoreactors, Microporous and Mesoporous Materials, 306, 306, 2020, Cites: 15
Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems
Slowik A., Cpalka K., Lapa K., Multipopulation Nature-Inspired Algorithm (MNIA) for the Designing of Interpretable Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 28, 28, 1125-1139, 2020, Cites: 11
An interpretable fuzzy system in the on-line signature scalable verification
Zalasinski M., Cpalka K., Lapa K., An interpretable fuzzy system in the on-line signature scalable verification, IEEE International Conference on Fuzzy Systems, 2020-July, 2020-July, 2020, Cites: 2
The Dynamic Signature Verification Using population-Based Vertical Partitioning
Zalasinski M., Cpalka K., Niksa-Rynkiewicz T., The Dynamic Signature Verification Using population-Based Vertical Partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12532 LNCS, 12532 LNCS, 569-579, 2020, Cites: 0
On-Line Signature Partitioning Using a Population Based Algorithm
Zalasinski M., Lapa K., Cpalka K., Przybyszewski K., Yen G.G., On-Line Signature Partitioning Using a Population Based Algorithm, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 5-13, 2020, Cites: 10
Synthesis in silica nanoreactor: Copper pyrophosphate quantum dots and silver oxide nanocrystallites inside silica mezochannels
Laskowski L., Majtyka-Pilat A., Cpalka K., Zubko M., Laskowska M., Synthesis in silica nanoreactor: Copper pyrophosphate quantum dots and silver oxide nanocrystallites inside silica mezochannels, Materials, 13, 13, 2020, Cites: 4
A Population-Based Method with Selection of a Search Operator
Lapa K., Cpalka K., Niksa-Rynkiewicz T., Wang L., A Population-Based Method with Selection of a Search Operator, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 429-444, 2020, Cites: 0
Signature Partitioning Using Selected Population-Based Algorithms
Zalasinski M., Cpalka K., Niksa-Rynkiewicz T., Hayashi Y., Signature Partitioning Using Selected Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12415 LNAI, 12415 LNAI, 480-488, 2020, Cites: 0
Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems
Slowik A., Cpalka K., Jin Y., Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems, IEEE Transactions on Fuzzy Systems, 28, 28, 1019-1022, 2020, Cites: 0
Evolutionary Algorithm with a Configurable Search Mechanism
Lapa K., Cpalka K., Laskowski L., Cader A., Zeng Z., Evolutionary Algorithm with a Configurable Search Mechanism, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 151-171, 2020, Cites: 10
An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors
Zalasinski M., Cpalka K., Laskowski L., Wunsch D.C., Przybyszewski K., An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors, Journal of Artificial Intelligence and Soft Computing Research, 10, 10, 173-187, 2020, Cites: 5

2019 (3)

The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms
Zalasinski M., Lapa K., Cpalka K., Marchlewska A., The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 540-549, 2019, Cites: 2
Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection
Lapa K., Cpalka K., Paszkowski J., Hybrid Multi-population Based Approach for Controllers Structure and Parameters Selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11508 LNAI, 11508 LNAI, 456-468, 2019, Cites: 3
Algorithm Based on Population with a Flexible Search Mechanism
Lapa K., Cpalka K., Zalasinski M., Algorithm Based on Population with a Flexible Search Mechanism, IEEE Access, 7, 7, 132253-132270, 2019, Cites: 5

2018 (11)

Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction
Lapa K., Cpalka K., Flexible Fuzzy PID Controller (FFPIDC) and a Nature-Inspired Method for Its Construction, IEEE Transactions on Industrial Informatics, 14, 14, 1078-1088, 2018, Cites: 23
Stability of features describing the dynamic signature biometric attribute
Zalasinski M., Cpalka K., Grzanek K., Stability of features describing the dynamic signature biometric attribute, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10842 LNAI, 10842 LNAI, 250-261, 2018, Cites: 0
Genetic programming algorithm for designing of control systems
Lapa K., Cpalka K., Przybyl A., Genetic programming algorithm for designing of control systems, Information Technology and Control, 47, 47, 668-683, 2018, Cites: 16
Negative space-based population initialization algorithm (NSPIA)
Lapa K., Cpalka K., Przybyl A., Grzanek K., Negative space-based population initialization algorithm (NSPIA), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10841 LNAI, 10841 LNAI, 449-461, 2018, Cites: 8
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
PID-fuzzy controllers with dynamic structure and evolutionary method for their construction
Lapa K., Cpalka K., PID-fuzzy controllers with dynamic structure and evolutionary method for their construction, Advances in Intelligent Systems and Computing, 655, 655, 138-148, 2018, Cites: 1
A method for genetic selection of the dynamic signature global features’ subset
Zalasinski M., Cpalka K., A method for genetic selection of the dynamic signature global features’ subset, Advances in Intelligent Systems and Computing, 655, 655, 73-82, 2018, Cites: 3
Evolutionary approach for automatic design of PID controllers
Lapa K., Cpalka K., Evolutionary approach for automatic design of PID controllers, Studies in Computational Intelligence, 738, 738, 353-373, 2018, Cites: 1
Prediction of values of the dynamic signature features
Zalasinski M., Lapa K., Cpalka K., Prediction of values of the dynamic signature features, Expert Systems with Applications, 104, 104, 86-96, 2018, Cites: 15
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
A new method for signature verification based on selection of the most important partitions of the dynamic signature
Zalasinski M., Cpalka K., A new method for signature verification based on selection of the most important partitions of the dynamic signature, Neurocomputing, 289, 289, 13-22, 2018, Cites: 11

2017 (16)

A method for genetic selection of the most characteristic descriptors of the dynamic signature
Zalasinski M., Cpalka K., Hayashi Y., A method for genetic selection of the most characteristic descriptors of the dynamic signature, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 747-760, 2017, Cites: 4
Introduction
Cpalka K., Introduction, Studies in Computational Intelligence, 684, 684, 1-10, 2017, Cites: 0
Concluding remarks and future perspectives
Cpalka K., Concluding remarks and future perspectives, Studies in Computational Intelligence, 684, 684, 191-193, 2017, Cites: 0
Stability evaluation of the dynamic signature partitions over time
Zalasinski M., Cpalka K., Er M.J., Stability evaluation of the dynamic signature partitions over time, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 733-746, 2017, Cites: 4
A method for changes prediction of the dynamic signature global features over time
Zalasinski M., Lapa K., Cpalka K., Saito T., A method for changes prediction of the dynamic signature global features over time, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 761-772, 2017, Cites: 3
Improving fuzzy systems interpretability by appropriate selection of their structure
Cpalka K., Improving fuzzy systems interpretability by appropriate selection of their structure, Studies in Computational Intelligence, 684, 684, 37-60, 2017, Cites: 1
Weighted fuzzy genetic programming algorithm for structure and parameters selection of fuzzy systems for nonlinear modelling
Lapa K., Cpalka K., Weighted fuzzy genetic programming algorithm for structure and parameters selection of fuzzy systems for nonlinear modelling, Advances in Intelligent Systems and Computing, 521, 521, 157-174, 2017, Cites: 0
Interpretability of fuzzy systems designed in the process of evolutionary learning
Cpalka K., Interpretability of fuzzy systems designed in the process of evolutionary learning, Studies in Computational Intelligence, 684, 684, 91-130, 2017, Cites: 7
A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators
Lapa K., Cpalka K., Wang L., A method for nonlinear fuzzy modelling using population based algorithm with flexibly selectable operators, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 263-278, 2017, Cites: 3
Hybrid initialization in the process of evolutionary learning
Lapa K., Cpalka K., Hayashi Y., Hybrid initialization in the process of evolutionary learning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10245 LNAI, 10245 LNAI, 380-393, 2017, Cites: 4
Selected topics in fuzzy systems designing
Cpalka K., Selected topics in fuzzy systems designing, Studies in Computational Intelligence, 684, 684, 11-25, 2017, Cites: 0
Case study: Interpretability of fuzzy systems applied to nonlinear modelling and control
Cpalka K., Case study: Interpretability of fuzzy systems applied to nonlinear modelling and control, Studies in Computational Intelligence, 684, 684, 131-162, 2017, Cites: 2
Fuzzy PID controllers with FIR filtering and a method for their construction
Lapa K., Cpalka K., Przybyl A., Saito T., Fuzzy PID controllers with FIR filtering and a method for their construction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 292-307, 2017, Cites: 4
Introduction to fuzzy system interpretability
Cpalka K., Introduction to fuzzy system interpretability, Studies in Computational Intelligence, 684, 684, 27-36, 2017, Cites: 1
Case study: Interpretability of fuzzy systems applied to identity verification
Cpalka K., Case study: Interpretability of fuzzy systems applied to identity verification, Studies in Computational Intelligence, 684, 684, 163-189, 2017, Cites: 0
Interpretability of fuzzy systems designed in the process of gradient learning
Cpalka K., Interpretability of fuzzy systems designed in the process of gradient learning, Studies in Computational Intelligence, 684, 684, 61-90, 2017, Cites: 0

2016 (10)

New method for fuzzy nonlinear modelling based on genetic programming
Lapa K., Cpalka K., Koprinkova-Hristova P., New method for fuzzy nonlinear modelling based on genetic programming, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 432-449, 2016, Cites: 5
New algorithm for on-line signature verification using characteristic hybrid partitions
Zalasinski M., Cpalka K., New algorithm for on-line signature verification using characteristic hybrid partitions, Advances in Intelligent Systems and Computing, 432, 432, 147-157, 2016, Cites: 27
A new approach to the dynamic signature verification aimed at minimizing the number of global features
Zalasinski M., Cpalka K., Hayashi Y., A new approach to the dynamic signature verification aimed at minimizing the number of global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 218-231, 2016, Cites: 18
Nonlinear pattern classification using fuzzy system and hybrid genetic-imperialist algorithm
Lapa K., Cpalka K., Nonlinear pattern classification using fuzzy system and hybrid genetic-imperialist algorithm, Advances in Intelligent Systems and Computing, 432, 432, 159-171, 2016, Cites: 4
New approach for interpretability of neuro-fuzzy systems with parametrized triangular norms
Lapa K., Cpalka K., Wang L., New approach for interpretability of neuro-fuzzy systems with parametrized triangular norms, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 248-265, 2016, Cites: 3
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
On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection
Lapa K., Cpalka K., On the application of a hybrid genetic-firework algorithm for controllers structure and parameters selection, Advances in Intelligent Systems and Computing, 429, 429, 111-123, 2016, Cites: 16
A new approach to nonlinear modelling of dynamic systems based on fuzzy rules
Bartczuk L., Przybyl A., Cpalka K., A new approach to nonlinear modelling of dynamic systems based on fuzzy rules, International Journal of Applied Mathematics and Computer Science, 26, 26, 603-621, 2016, Cites: 34
New approach for nonlinear modelling based on online designing of the fuzzy rule base
Lapa K., Cpalka K., Hayashi Y., New approach for nonlinear modelling based on online designing of the fuzzy rule base, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 230-247, 2016, Cites: 1
An idea of the dynamic signature verification based on a hybrid approach
Zalasinski M., Cpalka K., Rakus-Andersson E., An idea of the dynamic signature verification based on a hybrid approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 232-246, 2016, Cites: 28

2015 (4)

New fast algorithm for the dynamic signature verification using global features values
Zalasinski M., Cpalka K., Hayashi Y., New fast algorithm for the dynamic signature verification using global features values, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 175-188, 2015, Cites: 33
A new approach to design of control systems using genetic programming
Cpalka K., Lapa K., Przybyl A., A new approach to design of control systems using genetic programming, Information Technology and Control, 44, 44, 433-442, 2015, Cites: 40
A new method for the dynamic signature verification based on the stable partitions of the signature
Zalasinski M., Cpalka K., Er M.J., A new method for the dynamic signature verification based on the stable partitions of the signature, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 161-174, 2015, Cites: 16
A new interpretability criteria for neuro-fuzzy systems for nonlinear classification
Lapa K., Cpalka K., Galushkin A.I., A new interpretability criteria for neuro-fuzzy systems for nonlinear classification, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9119, 9119, 448-468, 2015, Cites: 11

2014 (7)

New method for dynamic signature verification based on global features
Zalasinski M., Cpalka K., Hayashi Y., New method for dynamic signature verification based on global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 231-245, 2014, Cites: 35
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 learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features
Nowicki R.K., Nowak B.A., Starczewski J.T., Cpalka K., The learning of neuro-fuzzy approximator with fuzzy rough sets in case of missing features, Proceedings of the International Joint Conference on Neural Networks, 3759-3766, 2014, Cites: 10
New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability
Lapa K., Cpalka K., Wang L., New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 217-232, 2014, Cites: 39
On-line signature verification using vertical signature partitioning
Cpalka K., Zalasinski M., On-line signature verification using vertical signature partitioning, Expert Systems with Applications, 41, 41, 4170-4180, 2014, Cites: 85
A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects
Cpalka K., Lapa K., Przybyl A., Zalasinski M., A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neurocomputing, 135, 135, 203-217, 2014, Cites: 80
New method for dynamic signature verification using hybrid partitioning
Zalasinski M., Cpalka K., Er M.J., New method for dynamic signature verification using hybrid partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 216-230, 2014, Cites: 33

2013 (7)

New approach for the on-line signature verification based on method of horizontal partitioning
Zalasinski M., Cpalka K., New approach for the on-line signature verification based on method of horizontal partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 342-350, 2013, Cites: 38
Novel algorithm for the on-line signature verification using selected discretization points groups
Zalasinski M., Cpalka K., Novel algorithm for the on-line signature verification using selected discretization points groups, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 493-502, 2013, Cites: 39
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
A new approach to designing interpretable models of dynamic systems
Lapa K., Przybyl A., Cpalka K., A new approach to designing interpretable models of dynamic systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 523-534, 2013, Cites: 45
A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling
Lapa K., Zalasinski M., Cpalka K., A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 329-344, 2013, Cites: 37
Some aspects of evolutionary designing optimal controllers
Szczypta J., Przybyl A., Cpalka K., Some aspects of evolutionary designing optimal controllers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 91-100, 2013, Cites: 35
New algorithm for evolutionary selection of the dynamic signature global features
Zalasinski M., Lapa K., Cpalka K., New algorithm for evolutionary selection of the dynamic signature global features, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7895 LNAI, 7895 LNAI, 113-121, 2013, Cites: 37

2012 (4)

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
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
A new method to construct of interpretable models of dynamic systems
Przybyl A., Cpalka K., A new method to construct of interpretable models of dynamic systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 697-705, 2012, Cites: 43
Novel algorithm for the on-line signature verification
Zalasinski M., Cpalka K., Novel algorithm for the on-line signature verification, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7268 LNAI, 7268 LNAI, 362-367, 2012, Cites: 45

2011 (1)

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 (2)

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
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

2009 (3)

A new method for design and reduction of neuro-fuzzy classification systems
Cpalka K., A new method for design and reduction of neuro-fuzzy classification systems, IEEE Transactions on Neural Networks, 20, 20, 701-714, 2009, Cites: 59
On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification
Cpalka K., On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification, Nonlinear Analysis, Theory, Methods and Applications, 71, 71, 2009, Cites: 67
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 (3)

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
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
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

2007 (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 (3)

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
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
A method for designing flexible neuro-fuzzy systems
Cpalka K., A method for designing flexible neuro-fuzzy systems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4029 LNAI, 4029 LNAI, 212-219, 2006, Cites: 40

2005 (4)

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
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 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

2004 (3)

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
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
A flexible connectionist fuzzy system
Cpalka K., A flexible connectionist fuzzy system, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3019, 3019, 618-625, 2004, Cites: 2

2003 (3)

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
Flexible neuro-fuzzy systems
Rutkowski L., Cpalka K., Flexible neuro-fuzzy systems, IEEE Transactions on Neural Networks, 14, 14, 554-574, 2003, Cites: 180
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 (2)

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
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 (1)

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

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