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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Distributed Dissipative State Estimation for Markov Jump Genetic Regulatory Networks Subject to Round-Robin Scheduling
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Distributed Dissipative State Estimation for Markov Jump Genetic Regulatory Networks Subject to Round-Robin Scheduling

机译：轮循调度的马尔可夫跳跃遗传调控网络的分布式耗散状态估计

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摘要

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 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.
机译：研究了基于循环调度的马尔可夫跳跃遗传调控网络的耗散状态估计问题。系统参数根据马尔可夫链随机变化。鉴于指定的网络拓扑图，传感器网络中的每个节点均与其相邻节点进行通信。采用循环调度来安排传输顺序，以减少发生数据冲突的可能性。这项工作的主要目的是设计一个兼容的分布式估计器，以确保分布式误差系统严格地是伽马随机耗散的。通过应用Lyapunov稳定性理论和改进的矩阵解耦方法，通过解决一些凸优化问题得出了充分条件。给出了一个说明性示例，以验证所提供方法的有效性。

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