Bipolar pattern association using a recurrent winner take all network

Author

McInroy, John E. ; Wilamowski, Bogdan M.

Author_Institution

Dept. of Electr. Eng., Wyoming Univ., Laramie, WY, USA

Volume

2

fYear

1997

fDate

9-12 Jun 1997

Firstpage

1231

Abstract

A neural network for heteroassociative (or autoassociative) pattern recognition of input bipolar binary vectors is proposed. By combining the advantages of feedforward and recurrent techniques for heteroassociation, a simple network with guaranteed error correction is found. The heart of the network is based on a new, recurrent method of performing the winner-take-all function. The analysis of this network leads to design rules which guarantee its performance. The network is tested on a character recognition problem utilizing the entire IBM CGA character set

Keywords

content-addressable storage; feedforward neural nets; pattern recognition; recurrent neural nets; IBM CGA character set; autoassociative pattern recognition; bipolar pattern association; character recognition; feedforward techniques; guaranteed error correction; heteroassociative pattern recognition; input bipolar binary vectors; recurrent techniques; recurrent winner-take-all network; Character recognition; Error correction; Hamming distance; Heart; Network topology; Neural networks; Pattern recognition; Performance analysis; Recurrent neural networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.616209

Filename

616209