Exponential stability of neural networks with distributed time delays and strongly nonlinear activation functions

Authors:
Chaojin Fu;Zhongsheng Wang
Affiliations:
Department of Mathematics, Hubei Normal University, Huangshi, Hubei, China;Department of Electric Engineering, ZhongYuan Institute of Technology, Zhengzhou, Henan, China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

In this paper, we provided a new technique based on the concept of comparison. Different from the Lyapunov method, the new technique showed that if the given conditions hold then the any state of neural networks with distributed time delays and strongly nonlinear activation functions is always bounded by exponential convergence function. In addition, some sufficient conditions are obtained to guarantee that such neural network is globally exponentially stable, or locally exponentially stable. Furthermore, we obtained the estimates of the exponential convergence rates and the region of exponential convergence.