Training matrix parameters by Particle Swarm Optimization using a fuzzy neural network for identification

Author

Shafiabady, Niusha ; Teshnehlab, M. ; Shooredeli, M. Aliyari

Author_Institution

Dept. of Mechatron. Eng. Technol., Azad Univ. Sci. & Res. center, Tehran

fYear

2007

fDate

25-28 Nov. 2007

Firstpage

188

Lastpage

193

Abstract

In this article particle swarm optimization that is a population-based method is applied to train the matrix parameters that are standard deviation and centers of radial basis function fuzzy neural network. We have applied least square and recursive least square in training the weights of this fuzzy neural network.There are four sets of data used to examine and prove that particle swarm optimization is a good method for training these complicated matrices as antecedent part parameters.

Keywords

fuzzy neural nets; least squares approximations; matrix algebra; particle swarm optimisation; radial basis function networks; fuzzy neural network; least square methods; particle swarm optimization; population-based method; radial basis function fuzzy neural network; recursive least square methods; training matrix parameters; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Intelligent networks; Intelligent systems; Least squares methods; Mechatronics; Neurons; Nonlinear control systems; Particle swarm optimization; Identification; Least Square; Particle Swarm Optimization; Radial Basis Function Fuzzy Neural Network; Recursive Least Square;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-1355-3

Electronic_ISBN

978-1-4244-1356-0

Type

conf

DOI

10.1109/ICIAS.2007.4658372

Filename

4658372