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Distributed mode-dependent state estimation for semi-Markovian jumping neural networks via sampled data

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  • Chao Ma
  • Wei Wu
  • Yinlin Li

Abstract

In this paper, a novel distributed state estimation scheme with sampled data is proposed for the semi-Markovian jumping neural networks (SMJNNs) with time-varying delays. In particular, mode-dependent distributed state estimators are designed to provide more flexibility. Based on the mode-dependent Lyapunov-Krasovskii functional, sufficient criteria are presented for ensuring the existence of the state estimators, based on which the desired mode-dependent estimator gains are further obtained. Finally, an illustrative example is presented for verifying the effectiveness and applicability of our theoretical results.

Suggested Citation

  • Chao Ma & Wei Wu & Yinlin Li, 2019. "Distributed mode-dependent state estimation for semi-Markovian jumping neural networks via sampled data," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(1), pages 216-230, January.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:1:p:216-230
    DOI: 10.1080/00207721.2018.1552771
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