Improved μ-state estimation for Markovian switching CVNNs with mixed delays: event-triggered mechanism

Abstract

In this paper, the μ-state estimation issue for Markovian switching complex-valued neural networks with mixed delays has been addressed. To conserve communication resources, an effective event-triggered mechanism is proposed, which relays on a Markovian switching positive threshold. By resorting to Lyapunov stability theory, stochastic analysis method and matrix inequalities technique, some sufficient criteria are established to guarantee the estimation error system is stochastic μ-stable in the mean sense. Moreover, the expected estimator gain matrices can be accurately achieved by solving some obtained matrix inequalities. Besides, it is should be pointed that the μ-stability involves exponential, power, and logarithmic stability, which expands a mass of existed relevant results. In the end, for power stability, one numerical example is provided to substantiate the effectiveness and credibility of the obtained theoretical results.

Keywords:

• Complex-valued neural networks
• Markovian switching
• μ-state estimation
• mixed delays
• event-triggered mechanism

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Additional information

Funding

This work was supported in part by the Fundamental Research Funds for the Central Universities, North Minzu University [grant number 2020KYQD17], and in part by the National Natural Science Foundation of China [grant numbers 61906084 and 12301237], and in part by the High-Level Talent Research Foundation of Anhui Agricultural University [grant number rc382106], and in part by the Philosophy and Social Science Foundation of Universities of Anhui Province [grant number 2023AH050970], and in part by the Natural Science Foundation of Universities of Anhui Province [grant number 2021A0175].