Two-Strategy reinforcement group cooperation based symbiotic evolution for TSK-type fuzzy controller design

This paper proposes a TSK-type fuzzy controller (TFC) with a two-strategy reinforcement group cooperation based symbiotic evolution (TSR-GCSE) for solving various control problems. The TSR-GCSE proposes the two-strategy reinforcement (TSR) signal designed to improve the performance of the traditional reinforcement signal designed. Moreover, the TSR-GCSE is different from the traditional symbiotic evolution; with each population in the TSR-GCSE method is divided to several groups. Each group represents a set of the chromosomes that belongs to a fuzzy rule and can cooperate with other groups to generation the better chromosomes by using elites-base compensation crossover strategy (ECCS). The illustrative examples show that the proposed method has the better time steps and CPU times than other existing methods.

[1]  L. D. Whitley,et al.  Genetic Reinforcement Learning for Neurocontrol Problems , 2004, Machine Learning.

[2]  Chin-Teng Lin,et al.  An ART-based fuzzy adaptive learning control network , 1997, IEEE Trans. Fuzzy Syst..

[3]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 2000, Springer Berlin Heidelberg.

[4]  Hamid R. Berenji,et al.  Learning and tuning fuzzy logic controllers through reinforcements , 1992, IEEE Trans. Neural Networks.

[5]  Cheng-Jian Lin,et al.  Prediction and identification using wavelet-based recurrent fuzzy neural networks , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Ka Cheok,et al.  A ball balancing demonstration of optimal and disturbance-accomodating control , 1987, IEEE Control Systems Magazine.

[7]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 2004 .

[8]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[9]  Ka Cheok,et al.  A ball balancing demonstration of optimal and disturbance-accomodating control , 1987 .

[10]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[11]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[12]  Cheng-Jian Lin,et al.  Efficient reinforcement learning through dynamic symbiotic evolution for TSK-type fuzzy controller design , 2005, Int. J. Gen. Syst..

[13]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[15]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  M.A. Lee,et al.  Integrating design stage of fuzzy systems using genetic algorithms , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[17]  Uzay Kaymak,et al.  Systems Control With Generalized Probabilistic Fuzzy-Reinforcement Learning , 2011, IEEE Transactions on Fuzzy Systems.

[18]  Chin-Teng Lin,et al.  GA-based fuzzy reinforcement learning for control of a magnetic bearing system , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[19]  Spyros G. Tzafestas,et al.  Fuzzy reinforcement learning control for compliance tasks of robotic manipulators , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[20]  Jude W. Shavlik,et al.  Extracting refined rules from knowledge-based neural networks , 2004, Machine Learning.

[21]  P. Kokotovic,et al.  Nonlinear control via approximate input-output linearization: the ball and beam example , 1992 .

[22]  Nicholas Bambos,et al.  A fuzzy reinforcement learning approach to power control in wireless transmitters , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Chia-Feng Juang,et al.  Combination of online clustering and Q-value based GA for reinforcement fuzzy system design , 2005, IEEE Transactions on Fuzzy Systems.

[24]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[25]  H. Takagi,et al.  Integrating Design Stages of Fuzzy Systems using Genetic Algorithms 1 , 1993 .

[26]  Cheng-Jian Lin,et al.  Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neurofuzzy Systems , 2007, IEEE Transactions on Fuzzy Systems.

[27]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[28]  W. Pinebrook.  The evolution of strategy. , 1990, Case studies in health administration.

[29]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[30]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[31]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[32]  Robert Babuska,et al.  Adaptive fuzzy control of satellite attitude by reinforcement learning , 1998, IEEE Trans. Fuzzy Syst..

[33]  Chin-Teng Lin,et al.  An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications , 1998 .

[34]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[35]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[36]  Chin-Teng Lin,et al.  Reinforcement learning for an ART-based fuzzy adaptive learning control network , 1996, IEEE Trans. Neural Networks.

[37]  Daniel W. C. Ho,et al.  Fuzzy wavelet networks for function learning , 2001, IEEE Trans. Fuzzy Syst..