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

  • Authors:

  • Miin-Tsair Su;Cheng-Hung Chen;Cheng-Jian Lin;Chin-Teng Lin

  • Affiliations:

  • Department of Electrical Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan, ROC;Department of Electrical Engineering, National Formosa University, Yunlin County 632, Taiwan, ROC;Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan, ROC;Department of Electrical Engineering, National Chiao-Tung University, Hsinchu 300, Taiwan, ROC

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2011

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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.