NDLI: An improved particle swarm optimization algorithm for radial basis function neural network

Content Provider	IEEE Xplore Digital Library
Author	Duan Qichang Zhao Min Duan Pan
Copyright Year	2009
Description	Author affiliation: College of Electrical Engineering, Chong qing University, 400044, China (Duan Pan) \|\| College of Automation, Chong qing University, 400044, China (Duan Qichang; Zhao Min)
Abstract	An improved particle swarm optimization (IMPSO) which synthesizes the existing models of constriction factor approach (CFA PSO) is proposed. In the proposed method, an adaptive algorithm based on the search space adjustable is applied to solve the problem that conventional particle swarm optimization (PSO) algorithm easily falls into local optimal and occur premature convergence. Then, the IMPSO is used to optimize the parameters of RBF neural network. The new training algorithm is used to approximate polynomial function and predict chaotic time series, compared with PSO, and CFA PSO, the algorithm speed up the speed of convergence, and has much greater accuracy.
Starting Page	2309
Ending Page	2313
File Size	105725
Page Count	5
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5192779
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos constriction factor Automation radial basis function neural network Vectors Electronic mail particle swarm optimization Particle swarm optimization Nearest neighbor searches Convergence nearest neighbor cluster algorithm Clustering algorithms Radial basis function networks Network synthesis
Content Type	Text
Resource Type	Article

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