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Discrete Dynamics in Nature and Society
Volume 2010, Article ID 810408, 19 pages
Research Article

Global Dissipativity on Uncertain Discrete-Time Neural Networks with Time-Varying Delays

1Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China
2Yangtze Center of Mathematics, Sichuan University, Chengdu 610064, China
3Department of Mathematics, Southeast University, Nanjing 210096, China

Received 24 December 2009; Accepted 14 February 2010

Academic Editor: Yong Zhou

Copyright © 2010 Qiankun Song and Jinde Cao. 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 problems on global dissipativity and global exponential dissipativity are investigated for uncertain discrete-time neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Illustrated examples are given to show the effectiveness of the proposed criteria. It is noteworthy that because neither model transformation nor free-weighting matrices are employed to deal with cross terms in the derivation of the dissipativity criteria, the obtained results are less conservative and more computationally efficient.