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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 289526, 9 pages
Research Article

State Estimation for Neural Networks with Leakage Delay and Time-Varying Delays

1Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China
2School of Civil Engineering & Architecture, Chongqing Jiaotong University, Chongqing 400074, China

Received 31 July 2013; Accepted 1 September 2013

Academic Editor: Jinde Cao

Copyright © 2013 Jing Liang 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.


The state estimation problem is investigated for neural networks with leakage delay and time-varying delay as well as for general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing matrix inequality techniques, a delay-dependent linear matrix inequalities (LMIs) condition is developed to estimate the neuron state with some observed output measurements such that the error-state system is globally asymptotically stable. An example is given to show the effectiveness of the proposed criterion.