Journal
NEURAL NETWORKS
Volume 22, Issue 1, Pages 41-48Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2008.09.015
Keywords
Neural networks; Markovian jumping systems; State estimation; Time-delays; Asymptotic stability; Linear Matrix inequalities
Funding
- Engineering and Physical Sciences Research Council (EPSRC) of the UK [GR/S27658/01]
- Royal Society of the UK
- National Natural Science Foundation of China [60774073, 60804028]
- Natural Science Foundation of jiangsu Province of China [BK2007075]
- Alexander von Humboldt Foundation of Germany
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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. (C) 2008 Elsevier Ltd. All rights reserved
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