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Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays

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Abstract

This paper is concerned with the mixed \(H_\infty\) and dissipativity performance for Markovian jump neural networks with time delay in the leakage term and randomly occurring uncertainties. The randomly occurring uncertainties are assumed to be mutually uncorrelated Bernoulli-distributed white noise sequences. By introducing a triple-integrable term in the Lyapunov functional, the Wirtinger-based double-integral inequality is utilized to bound the derivative of the triple-integral term and then a sufficient condition is derived to ensure that the considered neural networks to be strict \(({\mathcal {Q,S,R}}) - \gamma\)-dissipative and passive. These conditions are presented in terms of linear matrix inequalities, which can be easily solved by using standard numerical software. Finally, numerical examples are given to show the effectiveness and the potential significance of the proposed results.

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram, Tamil Nadu, 624302, India

    T. Radhika, G. Nagamani & S. Ramasamy

  2. Department of Science and Humanities-Mathematics, Karpagam University, Coimbatore, 641021, India

    T. Radhika

  3. School of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Quanxin Zhu

  4. Department of Mathematics, University of Bielefeld, 33615, Bielefeld, Germany

    Quanxin Zhu

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

    R. Saravanakumar

Authors
  1. T. Radhika
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  2. G. Nagamani
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  3. Quanxin Zhu
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  4. S. Ramasamy
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  5. R. Saravanakumar
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Corresponding author

Correspondence to G. Nagamani.

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Radhika, T., Nagamani, G., Zhu, Q. et al. Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays. Neural Comput & Applic 30, 3565–3579 (2018). https://doi.org/10.1007/s00521-017-2942-7

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  • DOI: https://doi.org/10.1007/s00521-017-2942-7

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