%0 Journal Article
	%A Miaomiao Yang and  Shouming Zhong
	%D 2014
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 85, 2014
	%T Improved Exponential Stability Analysis for Delayed Recurrent Neural Networks
	%U https://publications.waset.org/pdf/9997445
	%V 85
	%X This paper studies the problem of exponential stability analysis for recurrent neural networks with time-varying delay.By establishing a suitable augmented LyapunovCKrasovskii function and a novel sufficient condition is obtained to guarantee the exponential stability of the considered system.In order to get a less conservative results of the condition,zero equalities and reciprocally convex approach are employed. The several exponential stability criterion proposed in this paper is simpler and effective. A numerical example is provided to demonstrate the feasibility and effectiveness of our results.

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