Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/4939
Title: A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays
Authors: Song, Q
Wang, Z
Keywords: Discrete-time recurrent neural network;Time-varying delays;Periodic solution;Exponential stability;Lyapunov–Krasovskii functional;Linear matrix inequality
Issue Date: 2007
Publisher: Elsevier
Citation: Physics Letters A, 368 (1-2): 134-145, Aug 2007
Abstract: In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for a class of general discrete-time recurrent neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, and the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By employing the latest free-weighting matrix method, an appropriate Lyapunov–Krasovskii functional is constructed and several sufficient conditions are established to ensure the existence, uniqueness, and globally exponential stability of the periodic solution for the addressed neural network. The conditions are dependent on both the lower bound and upper bound of the time-varying time delays. Furthermore, the conditions are expressed in terms of the linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Two simulation examples are given to show the effectiveness and less conservatism of the proposed criteria.
Description: This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.
URI: http://bura.brunel.ac.uk/handle/2438/4939
DOI: http://dx.doi.org/10.1016/j.physleta.2007.03.088
ISSN: 0375-9601
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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