Stability of Delayed Hopfield Neural Networks with Variable-Time Impulses | Zendy

Yangjun Pei | Zendy; Chao Liu | Zendy; Qi Han | Zendy

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Stability of Delayed Hopfield Neural Networks with Variable-Time Impulses

Author(s) -

Yangjun Pei,

Chao Liu,

Qi Han

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/154036

Subject(s) - hopfield network , artificial neural network , variable (mathematics) , stability (learning theory) , exponential stability , computer science , control theory (sociology) , mathematics , mathematical optimization , artificial intelligence , machine learning , physics , mathematical analysis , control (management) , nonlinear system , quantum mechanics

In this paper the globally exponential stability criteria of delayed Hopfield neural networks with variable-time impulses are established. The proposed criteria can also be applied in Hopfield neural networks with fixed-time impulses. A numerical example is presented to illustrate the effectiveness of our theoretical results

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