论文标题
Kronecker网络的非参数识别
Nonparametric Identification of Kronecker Networks
论文作者
论文摘要
我们解决了该问题，以估计一个动态网络，其边缘描述了Granger因果关系关系，并且其拓扑具有Kronecker结构。这种结构在许多真实的网络中产生，并允许了解复杂网络的组织。我们提出了一种基于内核的PEM方法来学习此类网络。数值示例显示了所提出的方法的有效性。
We address the problem to estimate a dynamic network whose edges describe Granger causality relations and whose topology has a Kronecker structure. Such a structure arises in many real networks and allows to understand the organization of complex networks. We proposed a kernel-based PEM method to learn such networks. Numerical examples show the effectiveness of the proposed method.
