Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays

In this paper, the issue of event-triggered it-state estimation is addressed for a class of Markovian jumping neural networks (MJNNs) with mixed delays. The mixed delays involve both the infinitely distributed delay and the time-varying delay without requiring the upper bound, which has a distinction in existing conclusions and makes the model be more comprehensive. An event-triggered mechanism (ETM) with mode dependence is adopted to determine the appropriate updating instants of measurement outputs so as to alleviate the transmission of signals. By constructing a novel time-varying L-K functional with a general convergency rate and employing several analysis techniques, a sufficient criterion is obtained for ensuring the stochastic it-stability performance of error system, which is a more general stability performance including exponential stability, power stability as well as logarithmic stability as its special cases. Finally, three numerical examples are listed to demonstrate the effectiveness of the proposed method.(C) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper addresses the issue of event-triggered it-state estimation for a class of Markovian jumping neural networks with mixed delays. An event-triggered mechanism with mode dependence is adopted, and a criterion is obtained for ensuring the stochastic it-stability performance of the error system.