Improved Generalized H2 Filtering for Static Neural Networks with Time-Varying Delay via Free-Matrix-Based Integral Inequality

Abstract EN

This paper focuses on the generalized H2 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 H2 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 H2 filtering problem is established in terms of LMIs.

Finally, a numerical example is presented to show the effectiveness of the proposed method.