NDLI: Identification of Nonlinear System Based on a New Hybrid Gradient-Based PSO Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Sanfeng Chen Tao Mei Minzhou Luo Xiuqing Yang
Copyright Year	2007
Description	Author affiliation: Chinese Acad. of Sci., Hefei (Sanfeng Chen; Tao Mei; Minzhou Luo; Xiuqing Yang)
Abstract	Because the number of iterations necessary to locate the global best solution is not known a priori, it's problematic to make a proper choice of inertial weights omega and constriction coefficients l of particle swarm optimization (PSO) algorithm in advance. The existing PSO algorithms are sensitive to the above two parameters. In addition, standard PSO algorithms convergence slowly and coarsely in the latter period. A new hybrid PSO algorithm is proposed to overcome the above shortcomings. The new algorithm utilizes original PSO algorithm for locating approximately a good local minimum, and then a conjugate gradient based local search is done with the best solution found by the PSO algorithm as its starting point for finding local minimum accurately. A new optimization circle begins with the accurate local minimum as global best particle. The simulation results show that the new algorithm convergences more fast and accurately than GA. It also shows better performance than GA in identifying the parameters of RBF neural networks.
Starting Page	265
Ending Page	268
File Size	3063632
Page Count	4
File Format	PDF
ISBN	1424412196
DOI	10.1109/ICIA.2007.4295739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-08
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	RBF neural network Nonlinear control systems Control systems Educational institutions Electronic switching systems Particle swarm optimization PSO Genetic algorithms nonlinear system identification Biomimetics Neural networks Cities and towns Nonlinear systems conjugate gradient algorithm
Content Type	Text
Resource Type	Article

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