A new learning algorithm for RBF neural networks

Author

Man Chun-tao ; Yang Xu ; Zhang Li-yong

Author_Institution

Sch. of Autom., Harbin Univ. of Sci. & Technol., Harbin

fYear

2008

fDate

10-12 Dec. 2008

Firstpage

1

Lastpage

4

Abstract

A new method is presented in order to solve the problem of randomness of the initial selection for nearest neighbor clustering algorithm and redundant nodes introduced by subtractive clustering algorithm, namely, the algorithm that contain pruning technique of subtractive clustering algorithm and nearest neighbor clustering algorithm combine together,and accomplish the learning of training samples. The simulation results show that the effectiveness of the new algorithm.

Keywords

learning (artificial intelligence); pattern clustering; radial basis function networks; RBF neural networks; learning algorithm; nearest neighbor clustering algorithm; pruning technique; subtractive clustering algorithm; Approximation algorithms; Attenuation; Clustering algorithms; Convergence; Feedforward neural networks; Function approximation; Nearest neighbor searches; Neural networks; Radial basis function networks; Training data; Nearest Neighbor Clustering Algorithm; Pruning Technique; RBF Neural Network; Subtractive Clustering Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems and Control in Aerospace and Astronautics, 2008. ISSCAA 2008. 2nd International Symposium on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-3908-9

Electronic_ISBN

978-1-4244-2386-6

Type

conf

DOI

10.1109/ISSCAA.2008.4776251

Filename

4776251