This work focuses on the identifiability of dynamical networks with partial excitation and measurement: a set of nodes are interconnected by unknown transfer functions according to a known topology, some nodes are subject to external excitation, and some nodes are measured. The goal is to determine which transfer functions in the network can be recovered based on the input-output data collected from the excited and measured nodes.
We propose a local version of network identifiability, representing the ability to recover transfer functions which are approximately known, or to recover them up to a discrete ambiguity. We show that local identifiability is a generic property, establish a necessary and sufficient condition in terms of matrix generic ranks, and exploit this condition to develop an algorithm determining, with probability 1, which transfer functions are locally identifiable. Our implementation presents the results graphically, and is publicly available.
Identifiability of dynamical networks: which nodes need be measured?
Universidade Federal do Rio Grande do Sul（南里奥格兰德联邦大学）；Univsersité catholique de Louvain（天主教鲁汶大学）；Universidade Estadual do Rio Grande do Sul（南里奥格兰德州立大学）
Identification of Nonlinear Acyclic Networks in Continuous Time from Nonzero Initial Conditions and Full Excitations
Shiv Nadar University（希夫纳德大学）；Université de Lorraine, CNRS, CRAN（洛林大学、法国国家科学研究中心、CRAN）；UCLouvain（鲁汶天主教大学）；University of Namur（纳慕尔大学）；ICTEAM institute, UCLouvain（UCLouvain ICTEAM研究所）；Department of Mathematics, Namur Research Institute for Complex Systems, University of Namur（纳慕尔大学数学系、纳慕尔复杂系统研究所）
State Key Laboratory for Turbulence and Complex Systems, Department of Mechanics and Engineering Science, College of Engineering, Peking University（湍流与复杂系统国家重点实验室，力学与工程科学系，工程学院，北京大学）；Massachusetts Institute of Technology（麻省理工学院）；Queen’s University（皇后大学）；KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering, University of Cyprus（科斯卓越研究中心与创新中心，塞浦路斯大学电气与计算机工程系）
