DocumentCode
1557229
Title
A novel model of autoassociative memory and its self-organization
Author
Matsuoka, Kiyotoshi
Author_Institution
Div. of Control Eng., Kyushu Inst. of Technol., Japan
Volume
21
Issue
3
fYear
1991
Firstpage
678
Lastpage
683
Abstract
A network is presented which embodies the orthogonal type of association. A remarkable point of the network is that the weights of the connections between neurons can be determined directly from the correlation matrix derived from the prototype patterns, requiring no pseudoinverse calculation. As a result, the connection weights can also be obtained by an unsupervised, local learning procedure based on the conventional Hebbian principle
Keywords
content-addressable storage; learning systems; matrix algebra; Hebbian principle; autoassociative memory; connection weights; correlation matrix; local learning; model; neurons connection; self-organization; Backpropagation; Biological neural networks; Brain; Hopfield neural networks; Indium tin oxide; Learning systems; Neural networks; Organizing; Pattern recognition; Robots;
fLanguage
English
Journal_Title
Systems, Man and Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9472
Type
jour
DOI
10.1109/21.97460
Filename
97460
Link To Document