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Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays

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Abstract

This draft addresses the exponential stability problem for semi-Markovian jump generalized neural networks (S-MJGNNs) with interval time-varying delays. The exponential stability conditions are derived by establishing a suitable Lyapunov–Krasovskii functional and applying new analysis method. Improved results are obtained to guarantee the exponential stability of S-MJGNNs through improved reciprocally convex combination and new weighted integral inequality techniques. The method in this paper shows the advantages over some existing ones. To verify the advantages and benefits of employing proposed method is explained through numerical examples.

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Acknowledgments

This work was supported by the Thailand Research Fund (TRF) Grant No. RSA5980019, the Higher Education Commission and Faculty of Science, Maejo University, Thailand.

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Authors and Affiliations

  1. Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai, 50290, Thailand

    Grienggrai Rajchakit & R. Saravanakumar

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

    R. Saravanakumar

Authors
  1. Grienggrai Rajchakit
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  2. R. Saravanakumar
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Correspondence to R. Saravanakumar.

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Rajchakit, G., Saravanakumar, R. Exponential stability of semi-Markovian jump generalized neural networks with interval time-varying delays. Neural Comput & Applic 29, 483–492 (2018). https://doi.org/10.1007/s00521-016-2461-y

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  • DOI: https://doi.org/10.1007/s00521-016-2461-y

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