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A New Result on Stability Analysis of Recurrent Neural Networks with Time-Varying Delay Based on an Extended Delay-Dependent Integral Inequality

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Abstract

This paper studies the stability issue of recurrent neural networks (RNNs) with time-varying delay. Firstly, an extended delay-dependent integral inequality that contains more free matrices is presented, which is an extension of some existing delay-dependent integral inequalities. Secondly, by employing the extended delay-dependent integral inequality, a tight upper bound of the Lyapunov-Krasovskii functional (LKF) derivative is estimated, then a new criterion on stability analysis of delayed RNNs is obtained. Finally, simulation results are provided to verify the superiority of the presented method.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (grant no. 61973070), the Liaoning Revitalization Talents Program (grant no. XLYC1802010), and in part by SAPI Fundamental Research Funds (grant no. 2018ZCX22).

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  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Guoqiang Tan & Zhanshan Wang

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  1. Guoqiang Tan
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  2. Zhanshan Wang
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Correspondence to Zhanshan Wang.

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Tan, G., Wang, Z. A New Result on Stability Analysis of Recurrent Neural Networks with Time-Varying Delay Based on an Extended Delay-Dependent Integral Inequality. Neural Process Lett 53, 4365–4375 (2021). https://doi.org/10.1007/s11063-021-10601-y

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