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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/4948

Title: Exponential stability of delayed recurrent neural networks with Markovian jumping parameters
Authors: Wang, Z
Liu, Y
Yu, L
Iu, X
Keywords: Recurrent neural networks
Markovian jumping parameters
Time delays
Stochastic systems
Global exponential stability in the mean square
Linear matrix inequality
Publication Date: 2006
Publisher: Elsevier
Citation: Physics Letters A, 356(4-5): 346-352, Aug 2006
Abstract: In this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.
Description: This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.
Sponsorship: This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany.
URI: http://bura.brunel.ac.uk/handle/2438/4948
DOI: http://dx.doi.org/10.1016/j.physleta.2006.03.078
ISSN: 0375-9601
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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