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Non-fragile L2−L∞ filtering for a class of switched neural networks

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  • Tai, Weipeng
  • Zuo, Dandan
  • Xuan, Zuxing
  • Zhou, Jianping
  • Wang, Zhen

Abstract

This paper is devoted to non-fragile L2−L∞ filtering for switched neural networks with time-variant delay. The aim is to design a L2−L∞ filter subject to either additive or multiplicative gain perturbations, such that the filter-error system not only is asymptotically stable when there is no external disturbance but also has a predefined disturbance attenuation index under the zero initial condition. A criterion of the stability and L2−L∞ performance for the filter-error system is proposed by applying mode-dependent Lyapunov functionals, the Bessel–Legendre inequality, as well as the reciprocally convex combination technique. Then, a design method for the non-fragile L2−L∞ filter is developed by getting rid of some nonlinear coupling terms. The method is formulated as a problem of finding a feasible solution to a collection of linear matrix inequalities, which are computationally tractable. At last, two numerical examples are employed to illustrate the applicability of the L2−L∞ filtering design method.

Suggested Citation

  • Tai, Weipeng & Zuo, Dandan & Xuan, Zuxing & Zhou, Jianping & Wang, Zhen, 2021. "Non-fragile L2−L∞ filtering for a class of switched neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 629-645.
    • Handle: RePEc:eee:matcom:v:185:y:2021:i:c:p:629-645
    DOI: 10.1016/j.matcom.2021.01.014
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    References listed on IDEAS

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    1. Zhuang, Guangming & Xu, Shengyuan & Xia, Jianwei & Ma, Qian & Zhang, Zhengqiang, 2019. "Non-fragile delay feedback control for neutral stochastic Markovian jump systems with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 21-32.
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    3. Luo, Yiping & Deng, Fei & Ling, Zhaomin & Cheng, Zifeng, 2019. "Local H∞ synchronization of uncertain complex networks via non-fragile state feedback control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 335-346.
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    5. Sriraman, R. & Cao, Yang & Samidurai, R., 2020. "Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 171(C), pages 103-118.
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