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Neural Computing and Applications

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01.06.2014 | Original Article

Global exponential stability of a class of memristive neural networks with time-varying delays

verfasst von: Xin Wang, Chuandong Li, Tingwen Huang, Shukai Duan

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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Abstract

This paper studies the uniqueness and global exponential stability of the equilibrium point for memristor-based recurrent neural networks with time-varying delays. By employing Lyapunov functional and theory of differential equations with discontinuous right-hand side, we establish several sufficient conditions for exponential stability of the equilibrium point. In comparison with the existing results, the proposed stability conditions are milder and more general, and can be applied to the memristor-based neural networks model whose connection weight changes continuously. Numerical examples are also presented to show the effectiveness of the theoretical results.

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Titel: Global exponential stability of a class of memristive neural networks with time-varying delays
verfasst von: Xin Wang
Chuandong Li
Tingwen Huang
Shukai Duan
Publikationsdatum: 01.06.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-013-1383-1

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