A New Algorithm for Training an RBF Network

Author

Yang, Shao-qing ; Xiao, Yi ; Lin, Hong-wen

Author_Institution

Dept. of Inf. & Commun. Eng., Dalian Naval Acad.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3040

Lastpage

3043

Abstract

Artificial neural networks (ANNs) are important tools for function estimation. However, the existing ANNs for fitting functions at least have one of the following drawbacks: 1) low accuracy, 2) unstable training process, 3) long learning time, 4) too many hidden nodes. In this paper, based on the orthogonal least squares learning algorithm, a new approach is proposed which uses the gradient descent method to optimally determine the spread of RBFs for training an RBF network. The experimental results show the new method overcomes the above disadvantages even if fitting a chaotic signal

Keywords

estimation theory; function approximation; gradient methods; learning (artificial intelligence); least mean squares methods; radial basis function networks; RBF network; artificial neural network; chaotic signal; fitting function; function approximation; function estimation; gradient descent method; orthogonal least square learning algorithm; Artificial neural networks; Chaotic communication; Clustering algorithms; Cybernetics; Electronic mail; Function approximation; Least squares methods; Linearity; Machine learning; Machine learning algorithms; Radial basis function networks; Signal processing; Surface fitting; Surface waves; RBF network; chaotic signal; function approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258362

Filename

4028585