Entity

Time filter

Source Type


Li W.,Beihang University | Jia Y.,Beihang University | Jia Y.,Key Laboratory of Mathematics
IET Control Theory and Applications | Year: 2013

The paper proposed an adaptive filter for jump Markov systems with unknown measurement noise covariance. The filter is derived by treating covariance as a random matrix and an inverse-Wishart distribution is adopted as the conjugate prior. The variational Bayesian approximation method is employed to derive mode-conditioned estimates and mode-likelihood functions in the framework of interacting multiple model. A numerical example is provided to illustrate the performance of the proposed filter.© The Institution of Engineering and Technology 2013.


Zhang Z.,Central University of Finance and Economics | Zhang Z.,Key Laboratory of Mathematics | Li H.,Central University of Finance and Economics
Modern Physics Letters B | Year: 2016

Coupling centrality and authority of co-processing model on complex networks are investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are obtained to disclose the formation. Considering the influence of a node to the global dynamical behavior, coupling centrality and authority are introduced for each node, which determine the relative importance and authority of nodes in the diffusion process. Furthermore, the experimental results on large-scale complex networks confirm our analytical prediction. © World Scientific Publishing Company.


Zhang Z.,Central University of Finance and Economics | Zhang Z.,Key Laboratory of Mathematics
Modern Physics Letters B | Year: 2014

Diffusion processes have been widely investigated to understand some essential features of complex networks, and have attracted much attention from physicists, statisticians and computer scientists. In order to understand the evolution of the diffusion process and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the information entropy comprehending the structural characteristics and information propagation on the network. Based on the analysis of the diffusion process, we analyze the coupling impact of the structural factor and information propagating factor on the information entropy, where the analytical results fit well with the numerical ones on scale-free complex networks. The information entropy can better characterize the complex behaviors on networks and provides a new way to deepen the understanding of the diffusion process. © 2014 World Scientific Publishing Company.


Zhang Z.,Central University of Finance and Economics | Zhang Z.,Key Laboratory of Mathematics
Acta Physica Polonica B | Year: 2015

Information properties of co-processing model on communication networks are investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. Considering the influence of a node to the global dynamical behavior, co-processing centrality is introduced for each node, which determines the relative importance of nodes and exhibits the capability that a node communicates information with its neighbor environment over the network in the diffusion process. Furthermore, a new parameter, co-processing entropy, is proposed to measure the interplay between co-processing centrality and diffusion dynamics. At last, the information function of the co-processing model is investigated to deeply detect the properties of the diffusion process. The experimental results on large-scale complex networks with Poisson distribution confirm our analytical prediction.


Zhang Z.,Central University of Finance and Economics | Zhang Z.,Key Laboratory of Mathematics
Modern Physics Letters B | Year: 2015

Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks. © 2015 World Scientific Publishing Company.

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