Distributed Dissipative State Estimation for Markov Jump Genetic Regulatory Networks Subject to Round-Robin Scheduling

Hao Shen, Shicheng Huo, Huaicheng Yan, Ju H. Park, Victor Sreeram

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The distributed dissipative state estimation issue of Markov jump genetic regulatory networks subject to round-robin scheduling is investigated in this paper. The system parameters randomly change in the light of a Markov chain. Each node in sensor networks communicates with its neighboring nodes in view of the prescribed network topology graph. The round-robin scheduling is employed to arrange the transmission order to lessen the likelihood of the occurrence of data collisions. The main goal of the work is to design a compatible distributed estimator to assure that the distributed error system is strictly $(\Lambda _{1},\Lambda _{2},\Lambda _{3}) $ - $\gamma $ -stochastically dissipative. By applying the Lyapunov stability theory and a modified matrix decoupling way, sufficient conditions are derived by solving some convex optimization problems. An illustrative example is given to verify the validity of the provided method.

Original languageEnglish
Article number8703433
Pages (from-to)762-771
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number3
DOIs
Publication statusPublished - Mar 2020

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