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Mathematical Problems in Engineering
Volume 2014, Article ID 906439, 13 pages
Research Article

State Estimation for Discrete-Time Fuzzy Cellular Neural Networks with Mixed Time Delays

1Institute of Applied Mathematics, Shijiazhuang Mechanical Engineering College, Shijiazhuang 050003, China
2College of Mathematics and Information Technology, Xingtai University, Xingtai 054001, China

Received 26 December 2013; Accepted 10 March 2014; Published 29 April 2014

Academic Editor: Qintao Gan

Copyright © 2014 Lijie Geng 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.


This paper is concerned with the exponential state estimation problem for a class of discrete-time fuzzy cellular neural networks with mixed time delays. The main purpose is to estimate the neuron states through available output measurements such that the dynamics of the estimation error is globally exponentially stable. By constructing a novel Lyapunov-Krasovskii functional which contains a triple summation term, some sufficient conditions are derived to guarantee the existence of the state estimator. The linear matrix inequality approach is employed for the first time to deal with the fuzzy cellular neural networks in the discrete-time case. Compared with the present conditions in the form of -matrix, the results obtained in this paper are less conservative and can be checked readily by the MATLAB toolbox. Finally, some numerical examples are given to demonstrate the effectiveness of the proposed results.