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Mathematical Problems in Engineering
Volume 2014, Article ID 951973, 9 pages
http://dx.doi.org/10.1155/2014/951973
Research Article

Delay-Dependent State Estimation of Static Neural Networks with Time-Varying and Distributed Delays

1School of Electronics and Information Engineering, Soochow University, Suzhou 215006, China
2Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

Received 5 June 2014; Accepted 6 September 2014; Published 29 September 2014

Academic Editor: Chuandong Li

Copyright © 2014 Lei Shao 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 studying the state estimation problem of static neural networks with time-varying and distributed delays. By constructing a suitable Lyapunov functional and employing two integral inequalities, a sufficient condition is obtained under which the estimation error system is globally asymptotically stable. It can be seen that this condition is dependent on the two kinds of time delays. To reduce the conservatism of the derived result, Wirtinger inequality is employed to handle a cross term in the time-derivative of Lyapunov functional. It is further shown that the design of the gain matrix of state estimator is transformed to finding a feasible solution of a linear matrix inequality, which is efficiently facilitated by available algorithms. A numerical example is explored to demonstrate the effectiveness of the developed result.