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Title: State estimation for jumping recurrent neural networks with discrete and distributed delays
Authors: Wang, Z
Liu, Y
Liu, X
Keywords: Neural networks
Markovian jumping systems
State estimation
Asymptotic stability
Linear matrix inequalities
Publication Date: 2009
Publisher: Elsevier
Citation: Neural Networks, 22(1): 41-48, Jan 2009
Abstract: This paper is concerned with the state estimation problem for a class of Markovian neural networks with discrete and distributed time-delays. The neural networks have a finite number of modes, and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate the neuron states, through available output measurements, such that for all admissible time-delays, the dynamics of the estimation error is globally asymptotically stable in the mean square. An effective linear matrix inequality approach is developed to solve the neuron state estimation problem. Both the existence conditions and the explicit characterization of the desired estimator are derived. Furthermore, it is shown that the traditional stability analysis issue for delayed neural networks with Markovian jumping parameters can be included as a special case of our main results. Finally, numerical examples are given to illustrate the applicability of the proposed design method.
Description: This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier Ltd
Sponsorship: This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01, an International Joint Project sponsored by the Royal Society of the UK, the National Natural Science Foundation of China under Grants 60774073 and 60804028, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, and the Alexander von Humboldt Foundation of Germany.
ISSN: 0893-6080
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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