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\(H_{\infty }\) State Estimation of Static Neural Networks with Mixed Delay

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Abstract

This paper focuses on studying the \(H_{\infty }\) state estimation of static neural networks with mixed delay in which leakage time-varying delay and distributed delay are taken into account, simultaneously. By constructing several suitable Lyapunov–Krasovskii functionals and linear matrix inequality technique, the delay-independent and delay-dependent criteria are established in order that the error system is globally asymptotically stable with \(H_{\infty }\) performance, respectively. In addition, with the skills to construct Lyapunov–Krasovskii functionals, we obtain the results in which we constitutionally drop the differentiability requirement of transmission delays. Some numerical examples are given to show the effectiveness and advantages of the obtained results.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Shandong Normal University, Ji’nan, 250014, People’s Republic of China

    Shuchen Wu, Xiuping Han & Xiaodi Li

  2. Center for Control and Engineering Computation, Shandong Normal University, Ji’nan, 250014, People’s Republic of China

    Xiaodi Li

Authors
  1. Shuchen Wu
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  2. Xiuping Han
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  3. Xiaodi Li
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Correspondence to Xiaodi Li.

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This work is supported by National Natural Science Foundation of China (61673247), and the Research Fund for Distinguished Young Scholars and Excellent Young Scholars of Shandong Province (JQ201719). The paper has not been presented at any conference.

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Wu, S., Han, X. & Li, X. \(H_{\infty }\) State Estimation of Static Neural Networks with Mixed Delay. Neural Process Lett 52, 1069–1087 (2020). https://doi.org/10.1007/s11063-019-10171-0

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