 Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delaysCong Zou, Bing Li, Feiyang Liu and Bingrui Xu Applied Mathematics and Computation, 2022, vol. 425, issue C Abstract: In this paper, the issue of event-triggered μ-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 μ-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. Keywords: Markovian jumping neural networks; State estimation; Event-triggered mechanism; Unbounded time-varying delay; Stochastic μ-stability (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:425:y:2022:i:c:s0096300322001424 DOI: 10.1016/j.amc.2022.127056 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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