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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6692

Title: State estimation for discrete-time neural networks with Markov-mode-dependent lower and upper bounds on the distributed delays
Authors: Liu, Y
Wang, Z
Liu, X
Keywords: Discrete-time neural networks
Mixed time-delays
Markovian jumping parameters
Exponential stability
State estimate
Linear matrix inequality
Publication Date: 2012
Publisher: Springer Verlag
Citation: Neural Processing Letters, 36(1): 1 - 19, Aug 2012
Abstract: This paper is concerned with the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters and mixed time-delays. The parameters of the neural networks under consideration switch over time subject to a Markov chain. The networks involve both the discrete-time-varying delay and the mode-dependent distributed time-delay characterized by the upper and lower boundaries dependent on the Markov chain. By constructing novel Lyapunov-Krasovskii functionals, sufficient conditions are firstly established to guarantee the exponential stability in mean square for the addressed discrete-time neural networks with Markovian jumping parameters and mixed time-delays. Then, the state estimation problem is coped with for the same neural network where the goal is to design a desired state estimator such that the estimation error approaches zero exponentially in mean square. The derived conditions for both the stability and the existence of desired estimators are expressed in the form of matrix inequalities that can be solved by the semi-definite programme method. A numerical simulation example is exploited to demonstrate the usefulness of the main results obtained.
Description: Copyright @ 2012 Springer Verlag
Sponsorship: This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60774073 and 61074129, and the Natural Science Foundation of Jiangsu Province of China under Grant BK2010313.
URI: http://link.springer.com/article/10.1007/s11063-012-9219-z?null
http://bura.brunel.ac.uk/handle/2438/6692
DOI: http://dx.doi.org/10.1007/s11063-012-9219-z
ISSN: 1370-4621
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Computer Science
Dept of Computer Science Research Papers

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