Study on RBF NN based on improved differential evolution

Author

Dakuo, He ; Fuli, Wang ; Mingxing, Jia

Author_Institution

Key Lab. of Process Ind. Autom., Northeastern Univ., Shenyang, China

fYear

2009

fDate

17-19 June 2009

Firstpage

3508

Lastpage

3511

Abstract

A novel method of nonlinear system modeling using radial basis function neural network based on improved differential evolution algorithm is proposed. Differential evolution algorithm is presented to in order to improve modeling capability. Local operator and optimization selection strategy is presented to improve the searching speed and the local searching capability of genetic algorithm. According to the characteristics of radial basis function neural network and differential evolution algorithm, radial basis function neural network and differential evolution algorithm are associated to improve modeling precision. The simulation results show the effectiveness of this method.

Keywords

genetic algorithms; neurocontrollers; nonlinear control systems; radial basis function networks; search problems; RBF NN; genetic algorithm; improved differential evolution; local search capability; nonlinear system; optimization selection strategy; radial basis function neural network; Automation; Chromium; Electronic mail; Genetic algorithms; Helium; Laboratories; Neural networks; Nonlinear systems; Radial basis function networks; Improve Differential Evolution Algorithm; Local Operator; Nonlinear System; Radial Basis Function Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference, 2009. CCDC '09. Chinese

Conference_Location

Guilin

Print_ISBN

978-1-4244-2722-2

Electronic_ISBN

978-1-4244-2723-9

Type

conf

DOI

10.1109/CCDC.2009.5192492

Filename

5192492