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New Delay-Dependent Exponential Stability Criteria for Neural Networks with Mixed Time-Varying Delays

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  • Wu Wen
  • Kaibo Shi

Abstract

This study is concerned with the problem of new delay-dependent exponential stability criteria for neural networks (NNs) with mixed time-varying delays via introducing a novel integral inequality approach. Specifically, first, by taking fully the relationship between the terms in the Leibniz-Newton formula into account, several improved delay-dependent exponential stability criteria are obtained in terms of linear matrix inequalities (LMIs). Second, together with some effective mathematical techniques and a convex optimization approach, less conservative conditions are derived by constructing an appropriate Lyapunov-Krasovskii functional (LKF). Third, the proposed methods include the least numbers of decision variables while keeping the validity of the obtained results. Finally, three numerical examples with simulations are presented to illustrate the validity and advantages of the theoretical results.

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  • Wu Wen & Kaibo Shi, 2015. "New Delay-Dependent Exponential Stability Criteria for Neural Networks with Mixed Time-Varying Delays," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-16, September.
    • Handle: RePEc:hin:jnlmpe:767456
    DOI: 10.1155/2015/767456
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    Cited by:

    1. Shu, Jinlong & Wu, Baowei & Xiong, Lianglin & Wu, Tao & Zhang, Haiyang, 2021. "Stochastic stabilization of Markov jump quaternion-valued neural network using sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 400(C).

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