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A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design

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Abstract

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.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Chiao Tung University, Hsinchu City, Taiwan, Republic of China

    Sheng-Fuu Lin & Jyun-Wei Chang

  2. Graduate Institute of Network Learning Technology, National Central University, Jhongli City, Taiwan, Republic of China

    Yung-Chi Hsu

Authors
  1. Sheng-Fuu Lin
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  2. Jyun-Wei Chang
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  3. Yung-Chi Hsu
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Correspondence to Sheng-Fuu Lin.

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Lin, SF., Chang, JW. & Hsu, YC. A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design. Appl Intell 36, 454–471 (2012). https://doi.org/10.1007/s10489-010-0271-y

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  • DOI: https://doi.org/10.1007/s10489-010-0271-y

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