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Finite-Time Non-fragile Dissipative Stabilization of Delayed Neural Networks

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Abstract

This article studies the finite-time non-fragile dissipativity issue of time-delayed neural networks. The main purpose of this study is to synthesize the finite-time non-fragile and dissipativity controller guaranteeing the finite-time boundedness of the resulting neural networks (NNs) with optimal dissipative performance index. By constructing appropriate Lyapunov–Krasovskii functional, combining with Jensen’s inequality and Wirtinger’s based integral inequality, a new delay-dependent finite-time boundedness of dissipativity criteria is obtained in terms of linear matrix inequalities techniques. The finite-time non-fragile state-feedback controller is designed to ensure the strict dissipativeness of the concerned NNs. In addition, these conditions are obtained with less conservative results than those in the existing approaches, which has been shown through numerical examples.

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Authors and Affiliations

  1. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, 632115, India

    S. Saravanan & M. Syed Ali

  2. Department of Control and Robotics Engineering, Kunsan National University, Kunsan, 573-701, Republic of Korea

    R. Saravanakumar

Authors
  1. S. Saravanan
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  2. M. Syed Ali
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  3. R. Saravanakumar
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Correspondence to M. Syed Ali.

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Saravanan, S., Syed Ali, M. & Saravanakumar, R. Finite-Time Non-fragile Dissipative Stabilization of Delayed Neural Networks. Neural Process Lett 49, 573–591 (2019). https://doi.org/10.1007/s11063-018-9844-2

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