An improved genetic algorithm based fuzzy-tuned neural network

Ling, SH; Leung, FHF; Lam, HK

An improved genetic algorithm based fuzzy-tuned neural network

Ling, SH

Leung, FHF Lam, HK

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Publication Type:: Journal Article
Citation:: International Journal of Neural Systems, 2005, 15 (6), pp. 457 - 474
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Lam, HK	en_US
dc.date.available	2005-11-22	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	International Journal of Neural Systems, 2005, 15 (6), pp. 457 - 474	en_US
dc.identifier.issn	0129-0657	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15062
dc.description.abstract	This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA. © World Scientific Publishing Company.	en_US
dc.relation.ispartof	International Journal of Neural Systems	en_US
dc.relation.isbasedon	10.1142/S0129065705000438	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Neurons	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Mathematics	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Neural Networks (Computer)	en_US
dc.subject.mesh	Fuzzy Logic	en_US
dc.subject.mesh	Models, Genetic	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Neural Networks, Computer	en_US
dc.title	An improved genetic algorithm based fuzzy-tuned neural network	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	15	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	WOS:000234235100005	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

This paper presents a fuzzy-tuned neural network, which is trained by an improved genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a neuron model with two activation functions is used so that the degree of freedom of the network function can be increased. The neural-fuzzy network governs some of the parameters of the neuron model. It will be shown that the performance of the proposed fuzzy-tuned neural network is better than that of the traditional neural network with a similar number of parameters. An improved GA is proposed to train the parameters of the proposed network. Sets of improved genetic operations are presented. The performance of the improved GA will be shown to be better than that of the traditional GA. Some application examples are given to illustrate the merits of the proposed neural network and the improved GA. © World Scientific Publishing Company.

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http://hdl.handle.net/10453/15062