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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 585709, 16 pages
Research Article

Global Robust Exponential Dissipativity for Interval Recurrent Neural Networks with Infinity Distributed Delays

Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received 29 January 2013; Accepted 19 May 2013

Academic Editor: Chengming Huang

Copyright © 2013 Xiaohong Wang and Huan Qi. 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 robust dissipativity problem for interval recurrent neural networks (IRNNs) with general activation functions, and continuous time-varying delay, and infinity distributed time delay. By employing a new differential inequality, constructing two different kinds of Lyapunov functions, and abandoning the limitation on activation functions being bounded, monotonous and differentiable, several sufficient conditions are established to guarantee the global robust exponential dissipativity for the addressed IRNNs in terms of linear matrix inequalities (LMIs) which can be easily checked by LMI Control Toolbox in MATLAB. Furthermore, the specific estimation of positive invariant and global exponential attractive sets of the addressed system is also derived. Compared with the previous literatures, the results obtained in this paper are shown to improve and extend the earlier global dissipativity conclusions. Finally, two numerical examples are provided to demonstrate the potential effectiveness of the proposed results.