DocumentCode
288689
Title
Asymptotic hyperstability for the Hopfield model of neural networks
Author
Meyer-Bäse, Anke
Author_Institution
Inst. of Flight Mech. & Control, Tech. Hochschule Darmstadt, Germany
Volume
4
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
2436
Abstract
In this paper we survey and apply the hyperstability analysis to neural networks of Hopfield type. We derive general necessary conditions which have to be satisfied by the network parameters. The nonlinearity is not restricted to a sigmoid function. The hyperstability results include BIBO and Lyapunov stability statements
Keywords
Hopfield neural nets; Lyapunov methods; asymptotic stability; control nonlinearities; multivariable systems; nonlinear systems; BIBO system; Hopfield model; Lyapunov stability; Popov inequality; asymptotic hyperstability; multivariable systems; neural networks; nonlinearity; sigmoid function; Control theory; Hopfield neural networks; Neural networks; Nonlinear equations; Rails; Reactive power; Stability analysis; TV interference;
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.1994.374602
Filename
374602
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