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

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

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 185, issue C, 629-645

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.

Keywords: Neural networks; Time delay; Non-fragile filtering; L2−L∞ filtering (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:185:y:2021:i:c:p:629-645

DOI: 10.1016/j.matcom.2021.01.014

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