Using an Efficient Immune Symbiotic Evolution Learning for Compensatory Neuro-Fuzzy Controller

Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents an efficient immune symbiotic evolution learning (ISEL) algorithm for the compensatory neuro-fuzzy controller (CNFC). The proposed ISEL method includes three major components-initial population, subgroup symbiotic evolution, and immune system algorithm. First, the self-clustering algorithm that determines proper input space partitioning and finds the mean and variance of the Gaussian membership functions and number of rules is applied to the initial population. Second, the subgroup symbiotic evolution method that uses each subantibody represents a single fuzzy rule and the evolution of the rule itself. Third, the immune system algorithm uses the clonal selection principle, such that antibodies between others of high similar degree are canceled, and these antibodies, after processing, will have higher quality, accelerating the search, and increasing the global search capacity. Finally, the proposed CNFC with ISEL (CNFC-ISEL) method is adopted to solve several nonlinear control problems. The simulation results have shown that the proposed CNFC-ISEL can outperform other methods.
Original languageEnglish
Pages (from-to)668-682
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Volume17
Issue number3
DOIs
StatePublished - Jun 2009

Keywords

  • Compensatory fuzzy operator
  • immune system algorithm
  • neuro-fuzzy network
  • self-clustering algorithm (SCA)
  • symbiotic evolution

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