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On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
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  • On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
  • On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
저자명
Xing. Zong-Yi,Zhang. Yong,Hou. Yuan-Long,Jia. Li-Min
간행물명
International Journal of Control, Automation and Systems
권/호정보
2007년|5권 4호|pp.444-455 (12 pages)
발행정보
제어로봇시스템학회
파일정보
정기간행물|ENG|
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기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.