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Improved μ-state estimation for Markovian switching CVNNs with mixed delays: event-triggered mechanism

Author

Listed:
  • Tengfei Duan
  • Weiqiang Gong
  • Qiang Li
  • Kai Wang
  • Fanrong Sun

Abstract

In this paper, the μ-state estimation issue for Markovian switching complex-valued neural networks with mixed delays has been addressed. To conserve communication resources, an effective event-triggered mechanism is proposed, which relays on a Markovian switching positive threshold. By resorting to Lyapunov stability theory, stochastic analysis method and matrix inequalities technique, some sufficient criteria are established to guarantee the estimation error system is stochastic μ-stable in the mean sense. Moreover, the expected estimator gain matrices can be accurately achieved by solving some obtained matrix inequalities. Besides, it is should be pointed that the μ-stability involves exponential, power, and logarithmic stability, which expands a mass of existed relevant results. In the end, for power stability, one numerical example is provided to substantiate the effectiveness and credibility of the obtained theoretical results.

Suggested Citation

  • Tengfei Duan & Weiqiang Gong & Qiang Li & Kai Wang & Fanrong Sun, 2024. "Improved μ-state estimation for Markovian switching CVNNs with mixed delays: event-triggered mechanism," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(8), pages 1673-1692, June.
    • Handle: RePEc:taf:tsysxx:v:55:y:2024:i:8:p:1673-1692
    DOI: 10.1080/00207721.2024.2316249
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