Guaranteed recall of all training pairs for bidirectional associative memory

Author

Wang, Yeou-Fang ; Cruz, Jose B., Jr. ; Mulligan, J.H., Jr.

Author_Institution

Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA

Volume

2

Issue

6

fYear

1991

fDate

11/1/1991 12:00:00 AM

Firstpage

559

Lastpage

567

Abstract

Necessary and sufficient conditions are derived for the weights of a generalized correlation matrix of a bidirectional associative memory (BAM) which guarantee the recall of all training pairs. A linear programming/multiple training (LP/MT) method that determines weights which satisfy the conditions when a solution is feasible is presented. The sequential multiple training (SMT) method is shown to yield integers for the weights, which are multiplicities of the training pairs. Computer simulation results, including capacity comparisons of BAM, LP/MT BAM, and SMT BAM, are presented

Keywords

content-addressable storage; learning systems; linear programming; matrix algebra; neural nets; bidirectional associative memory; capacity comparisons; content addressable storage; generalized correlation matrix; linear programming; neural nets; sequential multiple training; training pairs; Application software; Associative memory; Computer simulation; Linear programming; Magnesium compounds; Manufacturing automation; Neural networks; Subcontracting; Sufficient conditions; Surface-mount technology;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.97933

Filename

97933