Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm

This paper proposes zero-order Takagi-Sugeno-Kang (TSK)-type fuzzy system learning using a two-phase swarm intelligence algorithm (TPSIA). The first phase of TPSIA learns fuzzy system structure and parameters by on-line clustering-aided ant colony optimization (ACO). Phase two aims to further optimize all of the free parameters in the fuzzy system using particle swarm optimization (PSO). In clustering-aided ACO (CACO), fuzzy system structure is learned through on-line clustering. Once a new rule is generated by clustering, the consequent is selected from a discrete set of candidate values by ACO. In ACO, the path of an ant is regarded as a combination of consequent values selected from every rule. CACO helps to locate good initial fuzzy systems for subsequent phase learning. In Phase two, initial particles in PSO are randomly generated according to the best solution found by CACO. All free parameters in the designed fuzzy system are optimally tuned by PSO. Simulations on fuzzy control of three nonlinear plants are conducted to verify TPSIA performance. Comparisons with other learning algorithms demonstrate TPSIA performance.

[1]  Abdollah Homaifar,et al.  Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[2]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[3]  Chia-Feng Juang,et al.  Fuzzy systems design by clustering-aided ant colony optimization for plant control , 2007, Int. J. Gen. Syst..

[4]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[5]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[6]  Martin C. Cooper.  Reduction operations in fuzzy or valued constraint satisfaction , 2003, Fuzzy Sets Syst..

[7]  Chia-Feng Juang.  Simultaneous structure and parameter design of fuzzy systems by hybridizing multi-group genetic algorithm and particle swarm optimization , 2006, J. Intell. Fuzzy Syst..

[8]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Kiyotaka Izumi,et al.  A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems , 2005, IEEE Transactions on Industrial Electronics.

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

[13]  Jorge Casillas,et al.  Learning cooperative linguistic fuzzy rules using the best–worst ant system algorithm: Research Articles , 2005 .

[14]  Cheng-Jian Lin,et al.  A GA-based neural fuzzy system for temperature control , 2004, Fuzzy Sets Syst..

[15]  Khaled Belarbi,et al.  Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach , 2000, IEEE Trans. Fuzzy Syst..

[16]  Konstantinos E. Parsopoulos,et al.  Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method , 2002 .

[17]  Thomas Stützle,et al.  Ant Colony Optimization , 2009, EMO.

[18]  Rudolf F. Albrecht,et al.  Artificial Neural Nets and Genetic Algorithms , 1995, Springer Vienna.

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

[20]  B. Turchiano,et al.  Combining Genetic Algorithms and Lyapunov-Based Adaptation for Online Design of Fuzzy Controllers , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[21]  Xin Chen,et al.  A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[23]  Christian Blum,et al.  Training feed-forward neural networks with ant colony optimization: an application to pattern classification , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[24]  Francisco Herrera,et al.  A three-stage evolutionary process for learning descriptive and approximate fuzzy-logic-controller knowledge bases from examples , 1997, Int. J. Approx. Reason..

[25]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[26]  Ching-Chang Wong,et al.  A self-generating method for fuzzy system design , 1999, Fuzzy Sets Syst..

[27]  Martin Pelikan,et al.  Performance of aggregation pheromone system on unimodal and multimodal problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

[28]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[29]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[30]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[31]  Chia-Feng Juang,et al.  A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms , 2002, IEEE Trans. Fuzzy Syst..

[32]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[33]  Mark Richards,et al.  Choosing a starting configuration for particle swarm optimization , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[34]  Russell C. Eberhart,et al.  Implementation of evolutionary fuzzy systems , 1999, IEEE Trans. Fuzzy Syst..

[35]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[36]  Chia-Feng Juang,et al.  Combination of online clustering and Q-value based GA for reinforcement fuzzy system design , 2005, IEEE Trans. Fuzzy Syst..

[37]  Jorge Casillas,et al.  Learning cooperative linguistic fuzzy rules using the best–worst ant system algorithm , 2005 .

[38]  Michael N. Vrahatis,et al.  Modification of the Particle Swarm Optimizer for Locating All the Global Minima , 2001 .

[39]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[40]  Chih-Hsun Chou,et al.  Genetic algorithm-based optimal fuzzy controller design in the linguistic space , 2006, IEEE Trans. Fuzzy Syst..