Abstract
This paper focuses on the generalizedH2filtering of static neural networks with a time-varying delay. The aim of this problem is to design a full-order filter such that the filtering error system is globally asymptotically stable with guaranteedH2performance index. By constructing an augmented Lyapunov-Krasovskii functional and applying the free-matrix-based integral inequality to estimate its derivative, an improved delay-dependent condition for the generalizedH2filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.
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