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Finite-Time Passivity of Stochastic Markov Jump Neural Networks with Random Distributed Delays and Sensor Nonlinearities

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Abstract

This paper investigates the problem of mean-square finite-time passivity of discrete-time stochastic Markov jump neural networks with distributed delays and sensor nonlinearities. The distributed delays and sensor nonlinearities are randomly varying and described by mode-dependent random sequences with known statistical information. The mean-square finite-time boundedness and mean-square finite-time passivity results are obtained based on Lyapunov-like functional method. And the mean-square finite-time passivity is also considered for neural networks with random infinite-distributed delays. Finally, a numerical example verifies the effectiveness of the method.

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Acknowledgements

This work was supported partially by the Zhejiang Provincial Natural Science Foundation of China under Grant LR16F030003, and the National Natural Science Foundation of China under Grants 61473107, U1509205, 61333009 and 61427808.

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Authors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yun Chen, Lele Yang & Anke Xue

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  1. Yun Chen
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  2. Lele Yang
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  3. Anke Xue
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Correspondence to Anke Xue.

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Chen, Y., Yang, L. & Xue, A. Finite-Time Passivity of Stochastic Markov Jump Neural Networks with Random Distributed Delays and Sensor Nonlinearities. Circuits Syst Signal Process 38, 2422–2444 (2019). https://doi.org/10.1007/s00034-018-0978-2

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