Publication: Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays
Issued Date
2017-07-01
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ISSN
13665820
00207179
00207179
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2-s2.0-85026414285
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Mahidol University
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SCOPUS
Bibliographic Citation
International Journal of Control. (2017), 1-21
Suggested Citation
M. Syed Ali, R. Vadivel, R. Saravanakumar Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays. International Journal of Control. (2017), 1-21. doi:10.1080/00207179.2017.1350884 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/42339
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Title
Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays
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Abstract
© 2017 Informa UK Limited, trading as Taylor & Francis Group This paper investigates the event-triggered state estimation problem of Markovian jumping impulsive neural networks with interval time-varying delays. The purpose is to design a state estimator to estimate system states through available output measurements. In the neural networks, there are a set of modes, which are determined by Markov chain. A Markovian jumping time-delay impulsive neural networks model is employed to describe the event-triggered scheme and the network- related behaviour, such as transmission delay, data package dropout and disorder. The proposed event-triggered scheme is used to determine whether the sampled state information should be transmitted. The discrete delays are assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. First, we design a state observer to estimate the neuron states. Second, based on a novel Lyapunov-Krasovskii functional (LKF) with triple-integral terms and using an improved inequality, several sufficient conditions are derived. The derived conditions are formulated in terms of a set of linear matrix inequalities, under which the estimation error system is globally asymptotically stable in the mean square sense. Finally, numerical examples are given to show the effectiveness and superiority of the results.