Delay-dependent exponential stability for a class of neural networks with time delays

Authors:
Shengyuan Xu;James Lam;Daniel W. C. Ho;Yun Zou
Affiliations:
Department of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China;Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road, Hong Kong;Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong;Department of Automation, Nanjing University of Science and Technology, Nanjing 210094, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2005

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Abstract

This paper is concerned with the exponential stability of a class of delayed neural networks described by nonlinear delay differential equations of the neutral type. In terms of a linear matrix inequality (LMI), a sufficient condition guaranteeing the existence, uniqueness and global exponential stability of an equilibrium point of such a kind of delayed neural networks is proposed. This condition is dependent on the size of the time delay, which is usually less conservative than delay-independent ones. The proposed LMI condition can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.