A new local training rule for higher-order associative memories

Author

Chang, Jyh-Yeong ; Liu, Wei-Hsien ; Lin, Jin-Kuan

Author_Institution

Dept. of Control Eng., Nat. Chiao Tung Univ., Hsinchu, Taiwan

Volume

2

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1056

Abstract

Necessary and sufficient conditions are derived for the correlation matrix and thresholds of higher-order associative memories (HOAMs) that can guarantee the recall of all training patterns. A local training rule is presented; this rule iteratively trains the correlation matrix and thresholds so that the complete recall conditions are satisfied. A design algorithm is proposed that ensures each training pattern is stored with as large a basin of attraction as possible. Computer simulations that demonstrate the power of the proposed local training rule are reported

Keywords

content-addressable storage; correlation methods; iterative methods; learning (artificial intelligence); matrix algebra; correlation matrix; higher-order associative memories; iterative method; local training rule; necessary condition; sufficient condition; thresholds; training patterns recall; Algorithm design and analysis; Associative memory; Computer errors; Computer simulation; Control engineering; Cost function; Iterative algorithms; Neural networks; Pattern recognition; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374329

Filename

374329