RBF Neural Network Identifier Based on Optimal Selection Cluster Algorithm and PSO Algorithm and its Application

Author

Xiang-jun, Duan ; Yan-Qin, Wang

Author_Institution

Mech. & Electr. Inst., Nanjing Coll. of Inf. Technol., Nanjing, China

Volume

1

fYear

2011

fDate

28-29 March 2011

Firstpage

884

Lastpage

887

Abstract

A model of RBF neural network (RBFNN) is framed to solve the problem of identification of nonlinear system. In order to realize the structure identification of RBFNN, a kind of hybrid parameter optimization algorithm is proposed based on optimal selection cluster algorithm and PSO. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. Then the structure and parameters optimization problem of RBFNN are solved using PSO. The algorithm is used in oilfield volcanic thickness modeling and prediction, results shows the validity of the algorithm.

Keywords

nonlinear systems; parameter estimation; particle swarm optimisation; pattern clustering; radial basis function networks; statistical analysis; RBF neural network identifier; hybrid parameter optimization algorithm; identification problem; nonlinear system; oilfield volcanic thickness prediction; optimal selection cluster algorithm; particle swarm optimization; structure identification; Algorithm design and analysis; Artificial neural networks; Clustering algorithms; Heuristic algorithms; Prediction algorithms; Radial basis function networks; Signal processing algorithms; Identification; Optimal selection cluster algorithm; Particle swarm optimization; RBF neural network (RBFNN);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on

Conference_Location

Shenzhen, Guangdong

Print_ISBN

978-1-61284-289-9

Type

conf

DOI

10.1109/ICICTA.2011.222

Filename

5750654