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State Estimation for Markovian Coupled Neural Networks with Multiple Time Delays Via Event-Triggered Mechanism

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Abstract

This paper focuses on the state estimation problem for a type of coupled neural networks with multiple time delays and markovian jumping communication topologies. To avoid unnecessary resources consuming, a novel state estimator is designed based on event-triggered mechanism, in which the control input of each node is only updated when the measurement output error exceeds a predefined threshold. The event-triggering time sequence is a subset of the switching time sequence, which can naturally excludes the Zeno-behavior. By utilizing an appropriate Lyapunov-Krasovskii functional, as well as the weak infinitesimal operator of Markov process and some algebraic inequalities, an easy-to-check sufficient criterion is derived to ensure the exponential ultimate boundedness of the estimation error. Finally, a simulation example is presented to illustrate the applications and effectiveness of the theoretical results.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants 61703210.

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Authors and Affiliations

  1. School of Information Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Yangling Wang

  2. Research Center for Complex Systems and Network Sciences, and Department of Mathematics, Southeast University, Nanjing, 210096, China

    Jinde Cao

  3. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Jinde Cao

  4. School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing, 211171, China

    Haijun Wang

Authors
  1. Yangling Wang
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  2. Jinde Cao
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  3. Haijun Wang
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Correspondence to Yangling Wang.

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Wang, Y., Cao, J. & Wang, H. State Estimation for Markovian Coupled Neural Networks with Multiple Time Delays Via Event-Triggered Mechanism. Neural Process Lett 53, 893–906 (2021). https://doi.org/10.1007/s11063-020-10396-4

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