Abstract and Applied Analysis

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

Xiaohong Wang and Huan Qi

Full-text: Open access

Abstract

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.

Article information

Source
Abstr. Appl. Anal., Volume 2013 (2013), Article ID 585709, 16 pages.

Dates
First available in Project Euclid: 27 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393511939

Digital Object Identifier
doi:10.1155/2013/585709

Mathematical Reviews number (MathSciNet)
MR3066626

Zentralblatt MATH identifier
1295.34075

Citation

Wang, Xiaohong; Qi, Huan. Global Robust Exponential Dissipativity for Interval Recurrent Neural Networks with Infinity Distributed Delays. Abstr. Appl. Anal. 2013 (2013), Article ID 585709, 16 pages. doi:10.1155/2013/585709. https://projecteuclid.org/euclid.aaa/1393511939


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