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

Author:

Yu Hui-Jun12,He Yong34ORCID,Wu Min34

Affiliation:

1. School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China

2. School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China

3. School of Automation, China University of Geosciences, Wuhan 430074, China

4. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China

Abstract

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.

Publisher

Hindawi Limited

Subject

General Engineering,General Mathematics

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