Improved generalized \(H_2\) filtering for static neural networks with time-varying delay via free-matrix-based integral inequality
Summary: This paper focuses on the generalized \(H_2\) filtering 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 guaranteed \(H_2\) performance 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 generalized \(H_2\) filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.
scientific article; zbMATH DE number 7019083