Contact:
Room: 522, https://telco.pcz.pl/and-d9x-drw ( w przypadku dostępu on-line)
Position:
Associate Professor
Classes:
Systemy wbudowane w układach sterowania wyk
Systemy wbudowane wyk
Programowanie systemów wbudowanych wyk
PhD DSc Eng
Andrzej Przybył
Office hours: Najbliższe konsultacje odbędą się w piątek, 15.09.2023 w godz. 11.15-12.00 w sali 522 w KISI. Następne odbęda się w poniedziałek 18.09.2023 w godz. 11.15-12.00. Będą to już ostatnie konsultacje przed zamknięciem sesji egzaminacyjnej.
Papers (30)
2023 (1)
Przybyl A., FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives. (0)
FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives
, FPGA-Based Optimization of Industrial Numerical Machine Tool Servo Drives, Electronics (Switzerland), 12, 12, 2023, Cites: 02021 (2)
Przybyl A., Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems. (6)
Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems
, Fixed-point arithmetic unit with a scaling mechanism for fpga-based embedded systems, Electronics (Switzerland), 10, 10, 2021, Cites: 6
Dziwinski P., Przybyl A., Trippner P., Paszkowski J., Hayashi Y., Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm. (5)
Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm
, Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm, Journal of Artificial Intelligence and Soft Computing Research, 11, 11, 243-266, 2021, Cites: 52018 (3)
Lapa K., Cpalka K., Przybyl A., Genetic programming algorithm for designing of control systems. (12)
Genetic programming algorithm for designing of control systems
, Genetic programming algorithm for designing of control systems, Information Technology and Control, 47, 47, 668-683, 2018, Cites: 12
Lapa K., Cpalka K., Przybyl A., Grzanek K., Negative space-based population initialization algorithm (NSPIA). (8)
Negative space-based population initialization algorithm (NSPIA)
, 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
Przybyl A., Hard real-time communication solution for mechatronic systems. (6)
Hard real-time communication solution for mechatronic systems
, Hard real-time communication solution for mechatronic systems, Robotics and Computer-Integrated Manufacturing, 49, 49, 309-316, 2018, Cites: 62017 (2)
Lapa K., Cpalka K., Przybyl A., Saito T., Fuzzy PID controllers with FIR filtering and a method for their construction. (4)
Fuzzy PID controllers with FIR filtering and a method for their construction
, 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
Przybyl A., Er M.J., A method for design of hardware emulators for a distributed network environment. (1)
A method for design of hardware emulators for a distributed network environment
, A method for design of hardware emulators for a distributed network environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10246 LNAI, 10246 LNAI, 318-336, 2017, Cites: 12016 (5)
Przybyl A., Szczypta J., Method of evolutionary designing of FPGA-based controllers. (5)
Method of evolutionary designing of FPGA-based controllers
, Method of evolutionary designing of FPGA-based controllers, Przeglad Elektrotechniczny, 92, 92, 174-179, 2016, Cites: 5
Przybyl A., Joo Er M., The method of hardware implementation of fuzzy systems on FPGA. (9)
The method of hardware implementation of fuzzy systems on FPGA
, The method of hardware implementation of fuzzy systems on FPGA, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 284-298, 2016, Cites: 9
Bartczuk L., Przybyl A., Cpalka K., A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. (34)
A new approach to nonlinear modelling of dynamic systems based on fuzzy rules
, 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
Przybyl A., Er M.J., A new approach to designing of intelligent emulators working in a distributed environment. (4)
A new approach to designing of intelligent emulators working in a distributed environment
, A new approach to designing of intelligent emulators working in a distributed environment, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9693, 9693, 546-558, 2016, Cites: 4
Przybyl A., Lapa K., Szczypta J., Wang L., The method of the evolutionary designing the elastic controller structure. (3)
The method of the evolutionary designing the elastic controller structure
, The method of the evolutionary designing the elastic controller structure, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9692, 9692, 476-492, 2016, Cites: 32015 (3)
Przybyl A., Szczypta J., Wang L., Optimization of controller structure using evolutionary algorithm. (2)
Optimization of controller structure using evolutionary algorithm
, Optimization of controller structure using evolutionary algorithm, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 261-271, 2015, Cites: 2
Cpalka K., Lapa K., Przybyl A., A new approach to design of control systems using genetic programming. (40)
A new approach to design of control systems using genetic programming
, A new approach to design of control systems using genetic programming, Information Technology and Control, 44, 44, 433-442, 2015, Cites: 40
Bartczuk L., Przybyl A., Koprinkova-Hristova P., New method for non-linear correction modelling of dynamic objects with genetic programming. (9)
New method for non-linear correction modelling of dynamic objects with genetic programming
, New method for non-linear correction modelling of dynamic objects with genetic programming, Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 9120, 9120, 318-329, 2015, Cites: 92014 (5)
Cpalka K., Lapa K., Przybyl A., Zalasinski M., A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. (80)
A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects
, A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neurocomputing, 135, 135, 203-217, 2014, Cites: 80
Dziwinski P., Bartczuk L., Przybyl A., Avedyan E.D., A new algorithm for identification of significant operating points using swarm intelligence. (32)
A new algorithm for identification of significant operating points using swarm intelligence
, A new algorithm for identification of significant operating points using swarm intelligence, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8468 LNAI, 8468 LNAI, 349-362, 2014, Cites: 32
Przybyl A., Er M.J., The idea for the integration of neuro-fuzzy hardware emulators with real-time network. (12)
The idea for the integration of neuro-fuzzy hardware emulators with real-time network
, The idea for the integration of neuro-fuzzy hardware emulators with real-time network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 279-294, 2014, Cites: 12
Bartczuk L., Przybyl A., Koprinkova-Hristova P., New method for nonlinear fuzzy correction modelling of dynamic objects. (26)
New method for nonlinear fuzzy correction modelling of dynamic objects
, New method for nonlinear fuzzy correction modelling of dynamic objects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 169-180, 2014, Cites: 26
Szczypta J., Przybyl A., Wang L., Evolutionary approach with multiple quality criteria for controller design. (12)
Evolutionary approach with multiple quality criteria for controller design
, Evolutionary approach with multiple quality criteria for controller design, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8467 LNAI, 8467 LNAI, 455-467, 2014, Cites: 122013 (3)
Szczypta J., Przybyl A., Cpalka K., Some aspects of evolutionary designing optimal controllers. (33)
Some aspects of evolutionary designing optimal controllers
, 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: 33
Bartczuk L., Przybyl A., Dziwinski P., Hybrid state variables - Fuzzy logic modelling of nonlinear objects. (19)
Hybrid state variables - Fuzzy logic modelling of nonlinear objects
, Hybrid state variables - Fuzzy logic modelling of nonlinear objects, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7894 LNAI, 7894 LNAI, 227-234, 2013, Cites: 19
Lapa K., Przybyl A., Cpalka K., A new approach to designing interpretable models of dynamic systems. (45)
A new approach to designing interpretable models of dynamic systems
, 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: 452012 (2)
Rutkowski L., Przybyl A., Cpalka K., Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. (89)
Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation
, Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation, IEEE Transactions on Industrial Electronics, 59, 59, 1238-1247, 2012, Cites: 89
Przybyl A., Cpalka K., A new method to construct of interpretable models of dynamic systems. (43)
A new method to construct of interpretable models of dynamic systems
, 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: 432010 (2)
Rutkowski L., Przybyl A., Cpalka K., Er M.J., Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. (53)
Online speed profile generation for industrial machine tool based on neuro-fuzzy approach
, Online speed profile generation for industrial machine tool based on neuro-fuzzy approach, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6114 LNAI, 6114 LNAI, 645-650, 2010, Cites: 53
PrzybyL A., Smolag J., Kimla P., Distributed control system based on real time ethernet for computer numerical controlled machine tool. (16)
Distributed control system based on real time ethernet for computer numerical controlled machine tool
, Distributed control system based on real time ethernet for computer numerical controlled machine tool, Przeglad Elektrotechniczny, 86, 86, 342-346, 2010, Cites: 162008 (1)
Jelonkiewicz J., Przybyl A., Accuracy improvement of neural network state variable estimator in induction motor drive. (5)
Accuracy improvement of neural network state variable estimator in induction motor drive
, Accuracy improvement of neural network state variable estimator in induction motor drive, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5097 LNAI, 5097 LNAI, 71-77, 2008, Cites: 52004 (1)
Jelonkiewicz J., Przybyl A., Influence of the training set selection on the performance of the neural network state variables estimators in the induction motor. (0)
