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Neural Processing Letters

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29-07-2021

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

Authors: Guoqiang Tan, Zhanshan Wang

Published in: Neural Processing Letters | Issue 6/2021

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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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Title: A New Result on Stability Analysis of Recurrent Neural Networks with Time-Varying Delay Based on an Extended Delay-Dependent Integral Inequality
Authors: Guoqiang Tan
Zhanshan Wang
Publication date: 29-07-2021
Publisher: Springer US
Published in: Neural Processing Letters / Issue 6/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-021-10601-y

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