A local linear wavelet neural network

Author

Chen, Yuehui ; Dong, Jiwen ; Yang, Bo ; Zhang, Yong

Author_Institution

Sch. of Inf. Sci. & Eng., Jinan Univ., China

Volume

3

fYear

2004

fDate

15-19 June 2004

Firstpage

1954

Abstract

This paper presents a local linear wavelet neural network. The difference of the network with the original wavelet neural network is that the connection weights between the hidden layer and output layer of the original WNN are replaced by a local linear model. A simple and fast training algorithm, particle swarm optimization (PSO), is also introduced for training the local linear wavelet neural network. Simulation results for the identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

Keywords

function approximation; identification; learning (artificial intelligence); neural nets; nonlinear dynamical systems; nonlinear functions; optimisation; wavelet transforms; identification; local linear wavelet neural network; network hidden layer; network output layer; nonlinear dynamical systems; nonlinear function approximation; particle swarm optimization; training algorithm; Algorithm design and analysis; Convergence; Frequency; Information science; Neural networks; Nonlinear systems; Optimization methods; Particle swarm optimization; Spline; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on

Print_ISBN

0-7803-8273-0

Type

conf

DOI

10.1109/WCICA.2004.1341921

Filename

1341921