Asymptotic Stability and Exponential Stability of Impulsive Delayed Hopfield Neural Networks | Zendy

Jing Chen | Zendy; Xiaodi Li | Zendy; Dequan Wang | Zendy

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Asymptotic Stability and Exponential Stability of Impulsive Delayed Hopfield Neural Networks

Author(s) -

Jing Chen,

Xiaodi Li,

Dequan Wang

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/638496

Subject(s) - exponential stability , mathematics , hopfield network , equilibrium point , artificial neural network , lyapunov function , stability (learning theory) , control theory (sociology) , stability criterion , exponential function , mathematical optimization , mathematical analysis , computer science , discrete time and continuous time , differential equation , artificial intelligence , nonlinear system , statistics , physics , control (management) , quantum mechanics , machine learning

A criterion for the uniform asymptotic stability of the equilibrium point of impulsive delayed Hopfieldneural networks is presented by using Lyapunov functions and linear matrix inequality approach. Thecriterion is a less restrictive version of a recent result. By means of constructing the extended impulsive Halanayinequality, we also analyze the exponential stability of impulsive delayed Hopfield neural networks. Some newsufficient conditions ensuring exponential stability of the equilibrium point of impulsive delayed Hopfield neuralnetworks are obtained. An example showing the effectiveness of the present criterion is given

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