DocumentCode
1123725
Title
Necessary and sufficient condition for absolute stability of neural networks
Author
Forti, Mauro ; Manetti, Stefano ; Marini, Mauro
Author_Institution
Dept. of Electron. Eng., Florence Univ., Italy
Volume
41
Issue
7
fYear
1994
fDate
7/1/1994 12:00:00 AM
Firstpage
491
Lastpage
494
Abstract
The main result in this paper is that for a neural circuit of the Hopfield type with a symmetric connection matrix T, the negative semidefiniteness of T is a necessary and sufficient condition for Absolute Stability. The most significant theoretical implication is that the class of neural circuits with a negative semidefinite T is the largest class of circuits that can be employed for embedding and solving optimization problems without the risk of spurious responses
Keywords
Hopfield neural nets; matrix algebra; optimisation; stability; Hopfield neural circuit; absolute stability; embedding; negative semidefiniteness; neural networks; optimization problems; spurious responses; symmetric connection matrix; Circuit stability; Hopfield neural networks; Integrated circuit interconnections; Neural networks; Neurons; Sampling methods; Shape; Sufficient conditions; Symmetric matrices; Traveling salesman problems;
fLanguage
English
Journal_Title
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
Publisher
ieee
ISSN
1057-7122
Type
jour
DOI
10.1109/81.298364
Filename
298364
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