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Journal of Control Science and Engineering
Volume 2016, Article ID 1759650, 11 pages
http://dx.doi.org/10.1155/2016/1759650
Research Article

Improved Results on State Estimation of Static Neural Networks with Time Delay

1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
2School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
3Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 29 September 2016; Accepted 10 November 2016

Academic Editor: Xian Zhang

Copyright © 2016 Bin Wen 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 studies the problem of state estimation for a class of delayed static neural networks. The purpose of the problem is to design a delay-dependent state estimator such that the dynamics of the error system is globally exponentially stable and a prescribed performance is guaranteed. Some improved delay-dependent conditions are established by constructing augmented Lyapunov-Krasovskii functionals (LKFs). The desired estimator gain matrix can be characterized in terms of the solution to LMIs (linear matrix inequalities). Numerical examples are provided to illustrate the effectiveness of the proposed method compared with some existing results.