Neuro-fuzzy system design using differential evolution with local information

Lin, CT; Han, MF; Lin, YY; Liao, SH; Chang, JY

Neuro-fuzzy system design using differential evolution with local information

Lin, CT

Han, MF Lin, YY Liao, SH Chang, JY

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2011, pp. 1003 - 1006
Issue Date:: 2011-09-27

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Field	Value	Language
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Han, MF	en_US
dc.contributor.author	Lin, YY	en_US
dc.contributor.author	Liao, SH	en_US
dc.contributor.author	Chang, JY	en_US
dc.date.issued	2011-09-27	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2011, pp. 1003 - 1006	en_US
dc.identifier.isbn	9781424473175	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119821
dc.description.abstract	This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZY.2011.6007522	en_US
dc.title	Neuro-fuzzy system design using differential evolution with local information	en_US
dc.type	Conference Proceeding
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.publication-status	Published	en_US

Abstract:

This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model. © 2011 IEEE.

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