Abstract
In this paper, the state estimation problem is investigated for a class of discrete-time delayed neural networks. The measurements, before they are received by the state estimator, are sampled and the sampling process is modeled by a Markov chain. In order to cater for more practical engineering, the transition probabilities of the Markov chain are considered to be partially available. A mode-dependent full-order state estimator is constructed and a sufficient condition is obtained under which the estimation error dynamics is exponentially ultimately bounded in the mean square. Meanwhile, an ultimate bound of the estimation error is estimated by seeking a root of an elementary equation. Subsequently, the desired estimators are designed in terms of the solution to a set of linear matrix inequalities. Finally, a numerical simulation example is presented and the desired estimator parameters are solved by using the Matlab toolboxes. The simulation illustrates the effectiveness of the proposed state estimation scheme.
Published Version
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