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Finite-Time Stability of Stochastic Cohen–Grossberg Neural Networks with Markovian Jumping Parameters and Distributed Time-Varying Delays

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Abstract

In this paper, the finite-time stability problem is considered for a class of stochastic Cohen–Grossberg neural networks (CGNNs) with Markovian jumping parameters and distributed time-varying delays. Based on Lyapunov–Krasovskii functional and stability analysis theory, a linear matrix inequality approach is developed to derive sufficient conditions for guaranteeing the stability of the concerned system. It is shown that the addressed stochastic CGNNs with Markovian jumping and distributed time varying delays are finite-time stable. An illustrative example is provided to show the effectiveness of the developed results.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Istanbul University, Avcilar, 34320, Istanbul, Turkey

    Emel Arslan

  2. Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, 632 115, India

    M. Syed Ali & S. Saravanan

Authors
  1. Emel Arslan
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  2. M. Syed Ali
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  3. S. Saravanan
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Correspondence to M. Syed Ali.

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Arslan, E., Ali, M.S. & Saravanan, S. Finite-Time Stability of Stochastic Cohen–Grossberg Neural Networks with Markovian Jumping Parameters and Distributed Time-Varying Delays. Neural Process Lett 46, 71–81 (2017). https://doi.org/10.1007/s11063-016-9574-2

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  • DOI: https://doi.org/10.1007/s11063-016-9574-2

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