Training type-2 fuzzy system by particle swarm optimization

Al-Jaafreh, MO; Al-Jumaily, AA

Training type-2 fuzzy system by particle swarm optimization

Al-Jaafreh, MO Al-Jumaily, AA

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Publication Type:: Conference Proceeding
Citation:: 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 3442 - 3446
Issue Date:: 2007-12-01

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Field	Value	Language
dc.contributor.author	Al-Jaafreh, MO	en_US
dc.contributor.author	Al-Jumaily, AA https://orcid.org/0000-0003-0297-2463	en_US
dc.date.issued	2007-12-01	en_US
dc.identifier.citation	2007 IEEE Congress on Evolutionary Computation, CEC 2007, 2007, pp. 3442 - 3446	en_US
dc.identifier.isbn	1424413400	en_US
dc.identifier.isbn	9781424413409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2722
dc.description.abstract	Many Intelligent techniques were established during last decades to handle nonlinear, multimode, noisy, nondifferentiable problems and to obtain optimum solution(s). This paper presents improving and implementations for two recently intelligent techniques; Type-2 Fuzzy System (T2 FS) and Particle Swarm Optimization (PSO) and presents a new method to optimize parameters of the primary membership functions of T2 FS by PSO to improve the performance and increase the accuracy of T2 FS model. The implementation of the suggested method on mean blood pressure estimation has very successful rate. © 2007 IEEE.	en_US
dc.relation.ispartof	2007 IEEE Congress on Evolutionary Computation, CEC 2007	en_US
dc.relation.isbasedon	10.1109/CEC.2007.4424917	en_US
dc.title	Training type-2 fuzzy system by particle swarm optimization	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
dc.location.activity	Singapore	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:

Many Intelligent techniques were established during last decades to handle nonlinear, multimode, noisy, nondifferentiable problems and to obtain optimum solution(s). This paper presents improving and implementations for two recently intelligent techniques; Type-2 Fuzzy System (T2 FS) and Particle Swarm Optimization (PSO) and presents a new method to optimize parameters of the primary membership functions of T2 FS by PSO to improve the performance and increase the accuracy of T2 FS model. The implementation of the suggested method on mean blood pressure estimation has very successful rate. © 2007 IEEE.

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http://hdl.handle.net/10453/2722