DocumentCode
883750
Title
A probabilistic model of neural networks with static attractors
Author
Yamanaka, Kazuo ; Agu, Masahiro
Author_Institution
Dept. of Precision Eng., Ibaraki Univ., Hitachi, Japan
Volume
20
Issue
4
fYear
1990
Firstpage
921
Lastpage
922
Abstract
A probabilistic version of the binary Hopfield networks is proposed. Operation of the network is completely parallel, in the sense that evolution of each unit is governed only by its inherent probabilistic law. It is shown that the global state is attracted by one of the equilibria with probability one
Keywords
neural nets; parallel processing; probability; binary Hopfield networks; global state; neural networks; probabilistic model; probability; static attractors; Neural networks; Precision engineering; Probability; State-space methods;
fLanguage
English
Journal_Title
Systems, Man and Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9472
Type
jour
DOI
10.1109/21.105090
Filename
105090
Link To Document