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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
Issue 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
ISSN: 1370-4621
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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