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In this paper, the variance-constrained finite-horizon state estimation problem is studied for a class of discrete time-varying systems with an event-triggering mechanism. An event-based transmission scheme is employed during the data communication from the sensor to the estimator with hope to reduce the network burden and energy consumption. Furthermore, redundant channels are utilized to enable...
In this paper, the state estimation problem is investigated for a class of discrete-time complex networks subject to nonlinearities, mixed delays, and stochastic noises. A set of event-based state estimators is constructed so as to reduce unnecessary data transmissions in the communication channel. Compared with the traditional state estimator whose measurement signal is received under a periodic...
In this paper, the state estimation problem is investigated for a class of discrete-time stochastic neural networks with event-triggered transmission (ETT) mechanism. Different from the traditional periodic communication mechanism, the ETT mechanism employed in this paper possesses the advantage of mitigating the network traffic resulting from the unnecessary sending and receiving data between the...
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