Modified probabilistic neural network hardware implementation schemes

Author

Zaknich, Anthony ; Attikiouzel, Yianni

Author_Institution

Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia

Volume

1

fYear

1996

fDate

26-29 Nov 1996

Firstpage

167

Abstract

The modified probabilistic neural network for nonlinear time series analysis was developed and introduced in 1991. It effectively represents a simple family of clustering methods for reducing the size of Specht´s general regression neural network and retaining all its benefits. Three hardware implementation schemes for the most basic form of the modified probabilistic neural network are described. The first is an optoelectronic implementation and the other two are very large scale integration designs: a virtual implementation and a fully parallel implementation

Keywords

VLSI; digital signal processing chips; feedforward neural nets; integrated optoelectronics; neural chips; optical neural nets; parallel architectures; time series; RBF neural networks; Specht general regression neural network; VLSI; clustering; nonlinear time series analysis; optoelectronics; probabilistic neural network; CMOS technology; Concurrent computing; Digital signal processing; Neural network hardware; Neural networks; Nonlinear equations; Optical signal processing; Parallel processing; Signal processing; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '96. Proceedings., 1996 IEEE TENCON. Digital Signal Processing Applications

Conference_Location

Perth, WA

Print_ISBN

0-7803-3679-8

Type

conf

DOI

10.1109/TENCON.1996.608775

Filename

608775