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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374602
