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

来源：Applied Intelligence, 2012, 36(2): 454-471.

DOI：10.1007/s10489-010-0271-y

• Genetic algorithm
• Fuzzy model
• Group-based symbiotic evolution
• Data mining
• Identification
• FP-Growth

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.

• 出版日期2012-3
• 单位中央民族大学