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

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...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2020-03, Vol.31 (3), p.762-771
Main Authors: Shen, Hao, Huo, Shicheng, Yan, Huaicheng, Park, Ju H., Sreeram, Victor
Format: Article
Language:English
Subjects:
Convexity
Decoupling
Design of experiments
Discrete-time genetic regulatory networks (GRN)
distributed dissipative state estimation
Genetics
Learning systems
Markov chains
Markov jump parameters
Markov processes
Network topologies
Network topology
Networks
Optimization
Robot sensing systems
round-robin scheduling (RRS)
Scheduling
State estimation
Topology
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Summary: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 (Λ 1 , Λ 2 , Λ 3 )y-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.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2019.2909747