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Robust dissipativity and passivity based state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays

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Abstract

In this paper, the problem of robust state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays is investigated based on dissipativity and passivity theory. The parameters of the neural networks are subject to the switching from one mode to another according to a Markov chain. By using the Lyapunov–Krasovskii functional together with linear matrix inequality approach, a new set of sufficient conditions are derived for the existence of state estimator such that the error state system is strictly \((\mathcal {Q}, \mathcal {S}, \mathcal {R})-\gamma \)-dissipative. Finally, numerical examples are addressed to show the effectiveness of the proposed design method .

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural Institute - Deemed University, Gandhigram, Tamilnadu, 624 302, India

    G. Nagamani & S. Ramasamy

  2. Department of Scientific Computing, Florida State University, Tallahassee, FL, 32306-4120, USA

    Anke Meyer-Baese

Authors
  1. G. Nagamani
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  2. S. Ramasamy
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  3. Anke Meyer-Baese
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Correspondence to G. Nagamani.

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Nagamani, G., Ramasamy, S. & Meyer-Baese, A. Robust dissipativity and passivity based state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays. Neural Comput & Applic 28, 717–735 (2017). https://doi.org/10.1007/s00521-015-2100-z

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  • DOI: https://doi.org/10.1007/s00521-015-2100-z

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