A new particle swarm optimization algorithm for neural network optimization

Ling, SH; Nguyen, HT; Chan, KY

A new particle swarm optimization algorithm for neural network optimization

Ling, SH

Nguyen, HT

Chan, KY

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Publication Type:: Conference Proceeding
Citation:: NSS 2009 - Network and System Security, 2009, pp. 516 - 521
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Chan, KY	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	NSS 2009 - Network and System Security, 2009, pp. 516 - 521	en_US
dc.identifier.isbn	9780769538389	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10726
dc.description.abstract	This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the value of the inertia weight becomes variable. The cross-mutated operation is effectively force the solution to escape the local optimum. Tuning parameters (weights) of neural networks is presented using the FPSOCM. Numerical example of neural network is given to illustrate that the performance of the FPSOCM is good for tuning the parameters (weights) of neural networks. © 2009 IEEE.	en_US
dc.relation.ispartof	NSS 2009 - Network and System Security	en_US
dc.relation.isbasedon	10.1109/NSS.2009.39	en_US
dc.title	A new particle swarm optimization algorithm for neural network optimization	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Gold Coast, Australia	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the value of the inertia weight becomes variable. The cross-mutated operation is effectively force the solution to escape the local optimum. Tuning parameters (weights) of neural networks is presented using the FPSOCM. Numerical example of neural network is given to illustrate that the performance of the FPSOCM is good for tuning the parameters (weights) of neural networks. © 2009 IEEE.

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http://hdl.handle.net/10453/10726