State estimation for neural networks with Markov-based nonuniform sampling: The partly unknown transition probability case

摘要

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. (C) 2019 Elsevier B.V. All rights reserved.