An Improved Particle Swarm Optimization Algorithm for Radial Basis Function Neural Network

被引：0

作者：

Duan Qichang ^{[1
]}

Zhao Min ^{[1
]}

Duan Pan ^{[2
]}

机构：

[1] Chongqing Univ, Coll Automat, Chongqing 400044, Peoples R China

[2] Chongqing Univ, Coll Elect Engn, Chongqing 400044, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

particle swarm optimization; radial basis function neural network; nearest neighbor cluster algorithm; constriction factor;

D O I：

10.1109/CCDC.2009.5192779

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：2309 / +

页数：2

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