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Reliable exponential H∞ filtering for a class of switched reaction-diffusion neural networks

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  • Yan, Zhilian
  • Guo, Tong
  • Zhao, Anqi
  • Kong, Qingkai
  • Zhou, Jianping

Abstract

In this paper, the reliable exponential H∞ filtering issue is studied for switched reaction-diffusion neural networks subject to exterior interference. The purpose is to design a Luenberger observer to make sure that the filtering error system possesses a pre-defined exponential H∞ interference-rejection level against possible sensor failures. An analysis result on the exponential H∞ performance is presented by the use of a Lyapunov functional together with a few inequalities. On its basis, a linear matrix inequalities-based design scheme for the Luenberger observer is proposed by getting rid of the nonlinear terms composed of the Lyapunov matrix, the gain matrix, and an uncertainty matrix caused by the sensor failures. In the case when the factors of sensor failures and reaction-diffusion are not concerned, the design scheme is shown to be an improvement over an existing design scheme. Finally, two examples are given to demonstrate the applicability and reduced conservatism of the obtained results, respectively.

Suggested Citation

  • Yan, Zhilian & Guo, Tong & Zhao, Anqi & Kong, Qingkai & Zhou, Jianping, 2022. "Reliable exponential H∞ filtering for a class of switched reaction-diffusion neural networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    • Handle: RePEc:eee:apmaco:v:414:y:2022:i:c:s0096300321007451
    DOI: 10.1016/j.amc.2021.126661
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    References listed on IDEAS

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    1. Liu, Yamin & Xuan, Zuxing & Wang, Zhen & Zhou, Jianping & Liu, Yajuan, 2020. "Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    2. K. Mathiyalagan & R. Ragul & Ju H. Park & J. Palraj, 2020. "Exponential stabilisation and dissipativity analysis of semilinear parabolic systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(12), pages 2181-2191, September.
    3. Jianping Zhou & Shengyuan Xu & Hao Shen & Baoyong Zhang, 2013. "Passivity analysis for uncertain BAM neural networks with time delays and reaction–diffusions," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(8), pages 1494-1503.
    4. Xing Yin & Jun Li & Weigen Wu & Qiranrong Tan, 2011. "Delay-Dependent Stability Criteria of Uncertain Periodic Switched Recurrent Neural Networks with Time-Varying Delays," Discrete Dynamics in Nature and Society, Hindawi, vol. 2011, pages 1-14, December.
    5. Zhang, Dian & Cheng, Jun & Cao, Jinde & Zhang, Dan, 2019. "Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties," Applied Mathematics and Computation, Elsevier, vol. 344, pages 230-242.
    6. Ren, Chengcheng & He, Shuping, 2020. "Finite-time stabilization for positive Markovian jumping neural networks," Applied Mathematics and Computation, Elsevier, vol. 365(C).
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    Cited by:

    1. Xueming Qian & Baotong Cui, 2022. "Mobile Sensor Networks for Finite-Time Distributed H ∞ Consensus Filtering of 3D Nonlinear Distributed Parameter Systems with Randomly Occurring Sensor Saturation," Mathematics, MDPI, vol. 10(17), pages 1-24, September.

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