Automatically extracting T-S fuzzy models using cooperative random learning particle swarm optimization

  • Authors:

  • Liang Zhao;Feng Qian;Yupu Yang;Yong Zeng;Haijun Su

  • Affiliations:

  • Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, No. 130, Meilong road, Xuhui District, Shanghai ...;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, No. 130, Meilong road, Xuhui District, Shanghai ...;Department of Automation, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, PR China;Department of Automation, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, PR China;Department of Automation, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Minhang District, Shanghai 200240, PR China

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2010

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Abstract

This paper proposes a methodology for automatically extracting T-S fuzzy models from data using particle swarm optimization (PSO). In the proposed method, the structures and parameters of the fuzzy models are encoded into a particle and evolve together so that the optimal structure and parameters can be achieved simultaneously. An improved version of the original PSO algorithm, the cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of PSO. CRPSO employs several sub-swarms to search the space and the useful information is exchanged among them during the iteration process. Simulation results indicate that CRPSO outperforms the standard PSO algorithm, genetic algorithm (GA) and differential evolution (DE) on the functions optimization and benchmark modeling problems. Moreover, the proposed CRPSO-based method can extract accurate T-S fuzzy model with appropriate number of rules.