Title: A Rule-Based Symbiotic MOdified Differential Evolution for Self-Organizing Neuro-Fuzzy Systems
Authors: Su, Miin-Tsair
Chen, Cheng-Hung
Lin, Cheng-Jian
Lin, Chin-Teng
電機工程學系
Department of Electrical and Computer Engineering
Keywords: Neuro-fuzzy systems;Symbiotic evolution;Differential evolution;Entropy measure;Control
Issue Date: 1-Dec-2011
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.
URI: http://dx.doi.org/10.1016/j.asoc.2011.06.015
http://hdl.handle.net/11536/14618
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2011.06.015
Journal: APPLIED SOFT COMPUTING
Volume: 11
Issue: 8
Begin Page: 4847
End Page: 4858
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