文献类型：期刊文献

英文题名：Automatically extracting T-S fuzzy models using cooperative random learning particle swarm optimization

作者：Zhao, Liang[1];Qian, Feng[1];Yang, Yupu[2];Zeng, Yong[2];Su, Haijun[2]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China

年份：2010

卷号：10

期号：3

起止页码：938

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20101012759381);WOS:【SCI-EXPANDED(收录号:WOS:000275377500027)】；

基金：The authors are grateful to the anonymous reviewers for their helpful comments and constructive suggestions with regard to this paper. This research is supported by National Science Fund for Distinguished Young Scholars of China (No. 60625302), Shanghai Leading Academic Discipline Project (No. B504) and Shanghai Key Technologies R& D Program (No. 08DZ1123100).

语种：英文

外文关键词：Particle swarm optimization; Cooperative random learning particle swarm optimization; Extracting; T-S fuzzy model; Fuzzy modeling

摘要：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. (C) 2009 Elsevier B.V. All rights reserved.