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Wang, Xiaohong; Qi, Huan

Global robust exponential dissipativity for interval recurrent neural networks with infinity distributed delays. (English) Zbl 1295.34075

Abstr. Appl. Anal. 2013, Article ID 585709, 16 p. (2013).
Summary: 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.

MSC:

34K25 Asymptotic theory of functional-differential equations
34K38 Functional-differential inequalities
92B20 Neural networks for/in biological studies, artificial life and related topics

Software:

Matlab; LMI toolbox
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