An immune symbiotic evolution learning for compensatory neural fuzzy networks and its applications

Chen, CH; Lin, CJ; Lin, CT

An immune symbiotic evolution learning for compensatory neural fuzzy networks and its applications

Chen, CH Lin, CJ Lin, CT

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Fuzzy Systems, 2011, pp. 2819 - 2826
Issue Date:: 2011-09-27

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Field	Value	Language
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	2011-09-27	en_US
dc.identifier.citation	IEEE International Conference on Fuzzy Systems, 2011, pp. 2819 - 2826	en_US
dc.identifier.isbn	9781424473175	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119502
dc.description.abstract	This study presents an efficient immune symbiotic evolution learning algorithm for the compensatory neural fuzzy network (CNFN). The proposed immune symbiotic evolution learning method (ISEL) includes three major components initial population, subgroup symbiotic evolution and immune system algorithm. The advantage of the proposed ISEL method are that the subgroup symbiotic evolution method uses the subgroup-based population to evaluate the fuzzy rules locally and the adopted immune system algorithm can accelerate the search and increase global search capacity. Finally, the simulation results have shown that the proposed CNFN-ISEL can outperform other methods. © 2011 IEEE.	en_US
dc.relation.ispartof	IEEE International Conference on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/FUZZY.2011.6007323	en_US
dc.title	An immune symbiotic evolution learning for compensatory neural fuzzy networks and its applications	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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 study presents an efficient immune symbiotic evolution learning algorithm for the compensatory neural fuzzy network (CNFN). The proposed immune symbiotic evolution learning method (ISEL) includes three major components initial population, subgroup symbiotic evolution and immune system algorithm. The advantage of the proposed ISEL method are that the subgroup symbiotic evolution method uses the subgroup-based population to evaluate the fuzzy rules locally and the adopted immune system algorithm can accelerate the search and increase global search capacity. Finally, the simulation results have shown that the proposed CNFN-ISEL can outperform other methods. © 2011 IEEE.

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http://hdl.handle.net/10453/119502