Title :
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
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;
Conference_Titel :
Signal Processing, 2002 6th International Conference on
Print_ISBN :
0-7803-7488-6
DOI :
10.1109/ICOSP.2002.1180015
