Function estimation using a neural-fuzzy network and an improved genetic algorithm

Lam, HK; Ling, SH; Leung, FHF; Tam, PKS

Function estimation using a neural-fuzzy network and an improved genetic algorithm

Lam, HK Ling, SH

Leung, FHF Tam, PKS

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Publication Type:: Journal Article
Citation:: International Journal of Approximate Reasoning, 2004, 36 (3), pp. 243 - 260
Issue Date:: 2004-07-01

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Field	Value	Language
dc.contributor.author	Lam, HK	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Tam, PKS	en_US
dc.date.issued	2004-07-01	en_US
dc.identifier.citation	International Journal of Approximate Reasoning, 2004, 36 (3), pp. 243 - 260	en_US
dc.identifier.issn	0888-613X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14484
dc.description.abstract	This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given. © 2003 Elsevier Inc. All rights reserved.	en_US
dc.relation.ispartof	International Journal of Approximate Reasoning	en_US
dc.relation.isbasedon	10.1016/j.ijar.2003.10.008	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Function estimation using a neural-fuzzy network and an improved genetic algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	36	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	WOS:000221630800003	en_US
dc.location.activity	WOS:000289952100337
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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

This paper presents the estimation of the transmission gains for an AC power line data network in an intelligent home. The estimated gains ensure the transmission reliability and efficiency. A neural-fuzzy network with rule switches is proposed to perform the estimation. An improved genetic algorithm is proposed to tune the parameters and the rules of the proposed neural-fuzzy network. By turning on or off the rule switches, an optimal rule base can be obtained. An application example will be given. © 2003 Elsevier Inc. All rights reserved.

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http://hdl.handle.net/10453/14484