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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 5147565, 9 pages
https://doi.org/10.1155/2018/5147565
Research Article

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

Hui-Jun Yu,1,2 Yong He,3,4 and Min Wu3,4

1School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China
2School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China
3School of Automation, China University of Geosciences, Wuhan 430074, China
4Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China

Correspondence should be addressed to Yong He

Received 18 July 2017; Revised 16 December 2017; Accepted 2 January 2018; Published 30 January 2018

Academic Editor: Renming Yang

Copyright © 2018 Hui-Jun Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper focuses on the generalized 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 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 filtering problem is established in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed method.