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A Variance-Constrained Method to Protocol-Based \(H_{\infty }\) State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity

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Abstract

In this paper, we consider the \(H_{\infty }\) state estimation issue under variance constraint for recurrent neural networks with time-varying parameters, where the time-delay as well as randomly occurring sensor nonlinearity are handled and the communication is scheduled by round-robin protocol. The phenomenon of randomly occurring sensor nonlinearity is modeled by a random variable obeying the Bernoulli distribution with known probability. In order to alleviate unnecessary network congestion in communication channels, the round-robin protocol is introduced to specify which network node has the right to access the network channel at each time step. In particular, the objective is to develop the time-varying state estimation method such that, in the presence of time-delay, randomly occurring sensor nonlinearity and round-robin protocol, the sufficient conditions are given and both the error variance boundedness and the pre-set \(H_{\infty }\) performance index can be achieved simultaneously. In the end, we provide a simulation example with comparison tests to demonstrate the feasibility of presented \(H_{\infty }\) state estimation method.

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Authors and Affiliations

  1. Department of Applied Mathematics, Harbin University of Science and Technology, Harbin, 150080, China

    Yan Gao & Jun Hu

  2. Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems, Harbin University of Science and Technology, Harbin, 150080, China

    Yan Gao, Jun Hu, Hui Yu & Chaoqing Jia

  3. School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

    Jun Hu, Cai Chen, Hui Yu & Chaoqing Jia

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  1. Yan Gao
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  5. Chaoqing Jia
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Contributions

YG and JH wrote the main manuscript text and prepared figures. CC did the simulation. HY and CJ checked the English. All authors reviewed the manuscript.

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Correspondence to Jun Hu or Cai Chen.

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This work was supported in part by the National Natural Science Foundation of China under Grant 12171124, the Natural Science Foundation of Heilongjiang Province of China under Grant ZD2022F003, and the Alexander von Humboldt Foundation of Germany.

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Gao, Y., Hu, J., Chen, C. et al. A Variance-Constrained Method to Protocol-Based \(H_{\infty }\) State Estimation for Delayed Neural Networks with Randomly Occurring Sensor Nonlinearity. Neural Process Lett 55, 12501–12523 (2023). https://doi.org/10.1007/s11063-023-11430-x

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