On global asymptotic stability for a class of delayed neural networks

Authors:
James Lam;Shengyuan Xu;Daniel W. C. Ho;Yun Zou
Affiliations:
Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road, Hong Kong;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China;Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong;School of Automation, Nanjing University of Science and Technology, Nanjing 210094, People's Republic of China
Venue:
International Journal of Circuit Theory and Applications
Year:
2012

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Abstract

This paper deals with the problem of stability analysis for a class of delayed neural networks described by nonlinear delay differential equations of the neutral type. A new and simple sufficient condition guaranteeing the existence, uniqueness and global asymptotic stability of an equilibrium point of such a kind of delayed neural networks is developed by the Lyapunov–Krasovskii method. The condition is expressed in terms of a linear matrix inequality, and thus can be checked easily by recently developed standard algorithms. When the stability condition is applied to the more commonly encountered delayed neural networks, it is shown that our result can be less conservative. Examples are provided to demonstrate the effectiveness of the proposed criteria. Copyright © 2011 John Wiley & Sons, Ltd.