Analysis of global exponential stability and periodic solutions of neural networks with time-varying delays

Authors:
He Huang;Daniel W. C. Ho;Jinde Cao
Affiliations:
Department of Computer Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China and Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Ko ...;Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, People's Republic of China;Department of Mathematics, Southeast University, Nanjing 210096, People's Republic of China
Venue:
Neural Networks
Year:
2005

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Abstract

In this paper, a general class of recurrent neural networks with time-varying delays is studied. Some novel and sufficient conditions are given to guarantee the global exponential stability of the equilibrium point and the existence of periodic solutions for such delayed neural networks. Comparing with some previous literature, in which the time-varying delays were assumed to be differentiable and their derivatives were simultaneously required to be not greater than 1, the restrictions on the time-varying delays are removed. Therefore, our results obtained here improve and extend some previously related results. Finally, two numerical examples are provided to illustrate our theorems.