Commenced in January 2007
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Improved Exponential Stability Analysis for Delayed Recurrent Neural Networks

Authors: Shouming Zhong, Miaomiao Yang

Abstract:

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.

Keywords: Neural Networks, linear matrix inequality, exponential stability, Lyapunov-Krasovskii, time-varying

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090761

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