A rule-based symbiotic modified differential evolution for self-organizing neuro-fuzzy systems

Su, MT; Chen, CH; Lin, CJ; Lin, CT

A rule-based symbiotic modified differential evolution for self-organizing neuro-fuzzy systems

Su, MT Chen, CH Lin, CJ Lin, CT

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Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2011, 11 (8), pp. 4847 - 4858
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Su, MT	en_US
dc.contributor.author	Chen, CH	en_US
dc.contributor.author	Lin, CJ	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Applied Soft Computing Journal, 2011, 11 (8), pp. 4847 - 4858	en_US
dc.identifier.issn	1568-4946	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116322
dc.description.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. © 2011 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Applied Soft Computing Journal	en_US
dc.relation.isbasedon	10.1016/j.asoc.2011.06.015	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A rule-based symbiotic modified differential evolution for self-organizing neuro-fuzzy systems	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	11	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

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. © 2011 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/116322