標題: A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design
作者: Lin, Sheng-Fuu
Chang, Jyun-Wei
Hsu, Yung-Chi
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Genetic algorithm;Fuzzy model;Group-based symbiotic evolution;Data mining;Identification;FP-Growth
公開日期: 1-三月-2012
摘要: In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.
URI: http://dx.doi.org/10.1007/s10489-010-0271-y
http://hdl.handle.net/11536/15591
ISSN: 0924-669X
DOI: 10.1007/s10489-010-0271-y
期刊: APPLIED INTELLIGENCE
Volume: 36
Issue: 2
起始頁: 454
結束頁: 471
顯示於類別:期刊論文


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