Probabilistic neural-network structure determination for pattern classification

Author

Mao, K.Z. ; Tan, K.-C. ; Ser, W.

Author_Institution

Centre for Signal Process., Nanyang Technol. Univ., Singapore

Volume

11

Issue

4

fYear

2000

fDate

7/1/2000 12:00:00 AM

Firstpage

1009

Lastpage

1016

Abstract

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy

Keywords

genetic algorithms; neural net architecture; pattern classification; classification accuracy; forward regression orthogonal algorithm; pattern layer neurons; probabilistic neural-network structure determination; smoothing parameter; supervised network structure; Clustering algorithms; Error analysis; Genetic algorithms; Iterative algorithms; Multi-layer neural network; Neural networks; Neurons; Pattern classification; Signal processing algorithms; Smoothing methods;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.857781

Filename

857781