A comprehensive understanding for radial basis probabilistic neural networks

Author

Huang, De-Shuang ; Zhao, Wenbo

Author_Institution

Inst. of Intelligent Machines, Acad. Sinica, Hefei, China

Volume

2

fYear

2002

fDate

26-30 Aug. 2002

Firstpage

1237

Abstract

The paper makes a profound analysis on radial basis probabilistic neural networks (RBPNN) from the viewpoint of linear algebra. Specifically, the transformation properties and internal representations of the RBPNNs are investigated in alliance with the properties of the input samples so that one may understand and grasp the mechanisms for pattern classification and function approximation of the RBPNNs. In addition, we analyse the convergence behaviour of the output class weight vectors of the RBPNNs, which can be shown to be orthogonal as well. Finally, one example for classifying five kinds of different distribution patterns are given to further support our understandings and claims.

Keywords

convergence of numerical methods; function approximation; linear algebra; pattern classification; radial basis function networks; distribution patterns; function approximation; internal representations; linear algebra; output class weight vectors; pattern classification; radial basis function networks; radial basis neural networks; radial basis probabilistic neural networks; transformation properties; Approximation algorithms; Feedforward neural networks; Function approximation; Hidden Markov models; Least squares approximation; Linear algebra; Mechanical factors; Neural networks; Pattern recognition; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing, 2002 6th International Conference on

Print_ISBN

0-7803-7488-6

Type

conf

DOI

10.1109/ICOSP.2002.1180015

Filename

1180015