Event-triggered state estimation for Markovian jumping neural networks: On mode-dependent delays and uncertain transition probabilities

This paper is concerned with the event-triggered state estimation (ETSE) problem for a class of discrete time Markovian jumping neural networks with mode-dependent time-delays and uncertain transition probabilities. The parameters of the neural networks experience switches that are characterized by a Markovian chain whose transition probabilities are allowed to be uncertain. The event-triggered mechanism is introduced in the sensor-to-estimator channel to reduce the frequency of signal communication. The aim of this paper is to develop an ETSE scheme such that the estimation error dynamics is exponentially ultimately bounded in the mean square. To achieve the aim, two sufficient conditions are proposed with the first one guaranteeing the existence of the required state estimator, and the second one giving the algorithm for designing the corresponding estimator gain by solving some matrix inequalities. In the end, the validity of the proposed estimation scheme is illustrated by a numerical example. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper investigates the event-triggered state estimation problem for discrete time Markovian jumping neural networks with mode-dependent time-delays and uncertain transition probabilities. The event-triggered mechanism is introduced to reduce the frequency of signal communication. Two sufficient conditions are proposed to design the estimator gain algorithm, aiming to ultimately bound the estimation error dynamics in the mean square.