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

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 3; pp. 762 - 771
• Main Authors: Shen, Hao, Huo, Shicheng, Yan, Huaicheng, Park, Ju H., Sreeram, Victor
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2909747

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.