State estimate via outputs from the fraction of nodes for discrete-time complex networks with Markovian jumping parameters and measurement noise

• Published in: Neurocomputing (Amsterdam) Vol. 508; pp. 99 - 108
• Main Authors: Liu, Yurong, Liu, Hongjian, Xue, Changfeng, Alotaibi, Naif D., Alsaadi, Fuad E.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 07.10.2022
• Subjects: Complex networks with measurement noise; Exponentially ultimately bounded estimator; Markovian jumping parameters; Mode-dependent distributed time-delay; State estimate based on a fraction of nodes; State estimate based on a fraction of nodes; Markovian jumping parameters; Exponentially ultimately bounded estimator; Complex networks with measurement noise; Mode-dependent distributed time-delay
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2022.08.054

This paper deals with the state estimate problem for a class of discrete-time complex networks. The target plant involves Markovian jumping parameters and mode-dependent distributed time-delays. It is assumed that the measurements for the estimate purpose are just from a portion of network nodes, and corrupted by a certain stochastic noise. A unified framework is developed to cope with the state estimation issue based on the outputs from the fraction of nodes for the discrete-time complex networks with distributed time delay and measurement noise. By employing the Lyapunov stability theory and some new techniques, the sufficient conditions are established to ensure the state estimation error is exponentially ultimately mean-square bounded. As the special case when the measurement is noise-free, the resulting criteria are to guarantee that the dynamics of state estimation error is exponentially stable. In addition, the estimator gain matrices are given in the explicit formula. Finally, numerical simulations are presented to illustrate the proposed approach.