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Partial-Neurons-Based \(H_{\infty }\) State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint

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Abstract

This paper discusses the issue of partial-neurons-based \(H_{\infty }\) state estimation for time-varying recurrent neural networks subject to randomly occurring time delays under variance constraint index. The measurement outputs are allowed to be available only at certain neurons. In addition, a random variable is introduced to model the randomly occurring time delays with certain occurrence probability. The aim is to propose the non-augmented partial-neurons-based state estimation strategy. Accordingly, some sufficient conditions are given to ensure two indices including the pre-determined \(H_{\infty }\) performance index and the error variance boundedness via the stochastic analysis approach. Finally, a simulation example is used to demonstrate the feasibility of presented partial-neurons-based \(H_{\infty }\) state estimation algorithm.

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Authors and Affiliations

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

    Jun Hu, Cai Chen & Chaoqing Jia

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

    Jun Hu, Yan Gao & Junhua Du

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

    Jun Hu, Yan Gao, Junhua Du & Chaoqing Jia

  4. College of Science, Qiqihar University, Qiqihar, 161006, China

    Junhua Du

Authors
  1. Jun Hu
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  2. Yan Gao
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  3. Cai Chen
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  5. Chaoqing Jia
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Jun Hu and Yan Gao wrote the main manuscript text and prepared figures. Cai Chen did the simulation. All authors reviewed the manuscript

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Correspondence to Jun Hu.

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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, the Postdoctoral Science Foundation of Heilongjiang Province of China under Grant LBH-Z22199, the Fundamental Research Funds in Heilongjiang Provincial Universities of China under Grant 135509121, the Educational Research Project of the Qiqihar University of China under Grant YB201904, and the Alexander von Humboldt Foundation of Germany.

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Hu, J., Gao, Y., Chen, C. et al. Partial-Neurons-Based \(H_{\infty }\) State Estimation for Time-Varying Neural Networks Subject to Randomly Occurring Time Delays under Variance Constraint. Neural Process Lett 55, 8285–8307 (2023). https://doi.org/10.1007/s11063-023-11312-2

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