Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks

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Title:	Hybrid Recursive Particle Swarm Optimization Learning Algorithm in the Design of Radial Basis Function Networks
Authors:	Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
Contributors:	淡江大學電機工程學系
Keywords:	normalized fuzzy c-means;particle swarm optimization;recursive least-squares;radial basis function networks
Date:	2007-03
Issue Date:	2010-08-09 19:44:44 (UTC+8)
Publisher:	Springer
Abstract:	In this paper, an innovative hybrid recursive particle swarm optimization (HRPSO) learning algorithm with normalized fuzzy c-mean (NFCM) clustering, particle swarm optimization (PSO) and recursive least-squares (RLS) is proposed to generate radial basis function networks (RBFNs) modeling system with small numbers of descriptive radial basis functions (RBFs) for fast approximating two complex and nonlinear functions. Simulation results demonstrate that the generated NFCM-based learning schemes approach the desired modeling systems within the smaller population sizes.
Relation:	Journal of Marine Science and Technology 15(1), pp.31-40
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Journal Article

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