Global Asymptotic Stability of High-Order Delay Hopfield Neural Networks with Time-Varying Coefficients | Zendy

Peiguang Wang | Zendy; Hairong Lian | Zendy; Yonghong Wu | Zendy

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Global Asymptotic Stability of High-Order Delay Hopfield Neural Networks with Time-Varying Coefficients

Author(s) -

Peiguang Wang,

Hairong Lian,

Yonghong Wu

Publication year - 2005

Publication title -

zeitschrift für analysis und ihre anwendungen

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.567

H-Index - 35

eISSN - 1661-4534

pISSN - 0232-2064

DOI - 10.4171/zaa/1249

Subject(s) - exponential stability , artificial neural network , equilibrium point , stability (learning theory) , mathematics , stability theorem , order (exchange) , fixed point theorem , control theory (sociology) , hopfield network , mathematical analysis , computer science , differential equation , nonlinear system , physics , economics , control (management) , finance , quantum mechanics , machine learning , artificial intelligence , cauchy distribution

In this paper, the problem of global asymptotic stability of the highorder delay neural networks with time-varying coefficients is investigated. Sufficient conditions are obtained for the existence and global asymptotic stability of the equilibrium of such neural networks by using Brouwer’s fixed point theorem and Liapunov method.

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