Dynamic network reconstruction from heterogeneous datasets. (January 2021)

• Record Type: Journal Article
• Authors: Yue, Zuogong; Thunberg, Johan; Pan, Wei; Ljung, Lennart; Gonçalves, Jorge
• Is Part Of: Automatica. Volume 123(2021)
• Publication Date: 2021-01
• Subjects: System identification -- Network reconstruction -- Heterogeneity -- Sparsity -- Multiple experiments; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2020.109339
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Performing multiple experiments is common when learning internal mechanisms of complex systems. These experiments can include perturbations of parameters or external disturbances. A challenging problem is to efficiently incorporate all collected data simultaneously to infer the underlying dynamic network. This paper addresses the reconstruction of dynamic networks from heterogeneous datasets under the assumption that the underlying networks share the same Boolean structure across all experiments. Parametric models are derived for dynamical structure functions, which describe causal interactions between measured variables. Multiple datasets are integrated into one regression problem with additional demands on group sparsity to assure network sparsity and structure consistency. To acquire structured group sparsity, we propose a sampling-based method, together with extended versions of l 1 -methods and sparse Bayesian learning. The performance of the proposed methods is benchmarked in numerical simulation. In summary, this paper presents efficient methods on network reconstruction from multiple experiments, and reveals practical experience that could guide applications.