Mode-mismatched estimator design for Markov jump genetic regulatory networks with random time delays

Autor:	Zhenzong Zhu, Elbaz I. Abouelmagd, Maryam Al-Yami, Yanzheng Zhu, Bashir Ahmad, Lixian Zhang
Rok vydání:	2015
Předmět:	Time delays Markov chain Cognitive Neuroscience Mode (statistics) Linear matrix inequality Estimator State (functional analysis) medicine.disease_cause Computer Science Applications Jumping Artificial Intelligence Control theory medicine Mathematics Markov jump
Zdroj:	Neurocomputing. 168:1121-1131
ISSN:	0925-2312
Popis:	In this paper, the problem of H ∞ state estimation is investigated for a class of discrete-time Markov jump genetic regulatory networks (GRNs) with random time delays. A mismatching characteristic of modes jumping between GRNs modes and desired mode-dependent estimators is recognized, and a nonstationary mode transition among the estimators is used to model the mismatching characteristic of modes jumping to different degrees. The time delays are supposed to be time-varying and subject to another Markov chain. By using the linear matrix inequality techniques, sufficient conditions on the existence of the estimators with mismatching characteristic of modes jumping are first derived such that the resulting estimation error system is stochastically stable with a prescribed H ∞ performance index. One interesting phenomenon is disclosed, i.e., the optimal performance index varies monotonously as changing the mismatching degrees of modes jumping. A numerical example is exploited to illustrate the effectiveness of the theoretical findings.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ee97eb28d49429bb323162f8712c421a https://doi.org/10.1016/j.neucom.2015.05.011 Zobrazit plný text záznamu Full Text from ScienceDirect