Sparse Bayesian Learning for Switching Network Identification
提出一种基于耦合超块的稀疏贝叶斯学习算法，利用时间和空间结构信息识别切换网络的未知切换时刻，在人工和真实基准网络上验证了有效性。
Learning dynamical networks based on time series of nodal states is of significant interest in systems science, computer science, and control engineering. Despite recent progress in network identification, most research focuses on static structures rather than switching ones. Therefore, this article develops a method for identifying the structures of switching networks by exploring and leveraging both temporal and spatial structural information that characterizes the switching process. The proposed method employs a new sparse Bayesian learning algorithm based on coupled hyperblocks to estimate unknown switching instants. Experimental results on benchmark artificial and real networks are elaborated to demonstrate the effectiveness and superiority of the proposed method.