Hybrid Adjusting Variables-Dependent Event-Based Finite-Time State Estimation for Two-Time-Scale Markov Jump Complex Networks

This article investigates the problem of dynamic event-triggered finite-time H_{\infty } state estimation for a class of discrete-time nonlinear two-time-scale Markov jump complex networks. A hybrid adjusting variables-dependent dynamic event-triggered mechanism (DETM) is proposed to regulate the...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-02, Vol.35 (2), p.1487-1500
Hauptverfasser:	Wan, Xiongbo, Yang, Chao, Zhang, Chuan-Ke, Wu, Min
Format:	Artikel
Sprache:	eng
Schlagworte:	Additives Couplings Dependent variables Design parameters Dynamic event-triggered mechanism (DETM) finite-time state estimation hybrid adjusting variables Learning systems Liapunov functions Manganese Markov jump complex networks (CNs) Markov processes Singular perturbation singularly perturbed system State estimation Symmetric matrices
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