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Delay-dependent dissipativity criteria for Markovian jump neural networks with random delays and incomplete transition probabilities

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Abstract

In this paper, a new double integral inequality which covers the well-known Wirtinger’s double integral inequality has been developed to analyze the dissipativity behavior of continuous-time neural networks involving Markovian jumping parameters with some unknown transition probabilities and random delays. Based on this generalized double integral inequality, the dissipativity conditions are proposed in terms of linear matrix inequalities by constructing an appropriate Lyapunov–Krasovskii functional with some multiple integral terms under the consideration of free-matrix-based integral inequality and Finsler’s lemma approach. Finally, the effectiveness and the advantages of the proposed technique have been exhibited through numerical simulations.

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Authors and Affiliations

  1. Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram, Tamil Nadu, 624 302, India

    G. Nagamani

  2. School of IT Technology and Control Engineering, Kunsan National University, Kunsan, Chonbuk, 573-701, Republic of Korea

    Young Hoon Joo

  3. Department of Science and Humanities-Mathematics, Karpagam University, Coimbatore, Tamil Nadu, 641 021, India

    T. Radhika

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  1. G. Nagamani
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  2. Young Hoon Joo
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  3. T. Radhika
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Correspondence to Young Hoon Joo.

Additional information

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A1A03013567) and by Korea Electric Power Corporation (Grant Number R17XA05-17).

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Nagamani, G., Joo, Y.H. & Radhika, T. Delay-dependent dissipativity criteria for Markovian jump neural networks with random delays and incomplete transition probabilities. Nonlinear Dyn 91, 2503–2522 (2018). https://doi.org/10.1007/s11071-017-4028-6

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  • DOI: https://doi.org/10.1007/s11071-017-4028-6

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