A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems

Miin-Tsair Su, Cheng-Hung Chen, Cheng-Jian Lin, Chin-Teng Lin

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for Self-Organizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure learning and parameter learning for the SONFS model. The structure learning can determine whether or not to generate a new rule-based subpopulation which satisfies the fuzzy partition of input variables using the entropy measure. The parameter learning combines two strategies including a subpopulation symbiotic evolution and a modified differential evolution. The RSMODE can automatically generate initial subpopulation and each individual in each subpopulation evolves separately using a modified differential evolution. Finally, the proposed method is applied in various simulations. Results of this study demonstrate the effectiveness of the proposed RSMODE learning algorithm. (C) 2011 Elsevier B. V. All rights reserved.
Original languageEnglish
Pages (from-to) 4847-4858
Number of pages12
JournalApplied Soft Computing
Volume11
Issue number8
DOIs
StatePublished - Dec 2011

Keywords

  • Neuro-fuzzy systems
  • Symbiotic evolution
  • Differential evolution
  • Entropy measure
  • Control

Fingerprint Dive into the research topics of 'A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems'. Together they form a unique fingerprint.

Cite this