Parallel neural network architectures

Author

Guoyin, Wang ; Hongbao, Shi

Author_Institution

Dept. of Comput. Sci., Xi´´an Jiaotong Univ., China

Volume

3

fYear

1995

fDate

Nov/Dec 1995

Firstpage

1234

Abstract

Several parallel neural network (PNN) architectures are presented in this paper. PNNs can work parallelly and coordinately. The implementation of their training is much easier than that of a single NN. And there are many other attractive characteristics of PNNs such as a modular structure, easy implementation by hardware, high efficiency for their parallel structures (compared with sequential NN architectures), easy implementation of additional learning, etc. PNNs can be used to deal with such problems as data processing, pattern recognition, and classification. The learning and additional learning algorithms for PNNs are presented in this paper. Some simulation results are given to illustrate the advantages of all the PNNs considered

Keywords

learning (artificial intelligence); neural net architecture; parallel processing; additional learning; modular structure; neural network architectures; parallel neural network; parallel processing; Biological neural networks; Competitive intelligence; Computer architecture; Control systems; Humans; Intelligent systems; Neural networks; Neurons; Pattern recognition; Switches;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.487331

Filename

487331