A RBF neural network learning algorithm based on NCPSO

Author

Xue Lei ; Sun Changyin ; Mu Chaoxu ; Huang Yiqing

Author_Institution

Sch. of Autom., Southeast Univ., Nanjing, China

fYear

2013

Firstpage

3294

Lastpage

3299

Abstract

A RBF (Radial Basis Function) neural network learning algorithm based on NCPSO (Niching Chaotic Mutation Particle Swarm Optimization) is proposed. Because of the niching method and chaotic mutation, NCPSO can be used to optimize the output weights of the RBF Neural Network. Niching method is introduced to improve the ability of global optimization. Chaotic mutation is mentioned to improve the solution. Compared with the RBF neural network learning algorithm based on the GA (Genetic Algorithm), simulation shows that the RBF neural network learning algorithm based on NCPSO mentioned in this paper has a lower error of tracking and higher speed.

Keywords

genetic algorithms; learning (artificial intelligence); particle swarm optimisation; radial basis function networks; GA; NCPSO; RBF neural network learning algorithm; genetic algorithm; neural network output weights; niching chaotic mutation particle swarm optimization; niching method; radial basis function neural network; Algorithm design and analysis; Equations; Genetic algorithms; Mathematical model; Neural networks; Optimization; Particle swarm optimization; Evolutionary Computational Techniques; Genetic Algorithm; Niching Chaotic Mutation Particle Swarm Optimization; Radial Basis Function Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2013 32nd Chinese

Conference_Location

Xi´an

Type

conf

Filename

6639989