Optimal Selection of Observations for Identification of Multiple Modules in Dynamic Networks

• 成果类型: Article
• 署名作者: Jahandari, Sina; Materassi, Donatello
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3179213
• 发表日期: 2022
• 页码: 4703-4716
• 关键词: Transfer functions; Object recognition; Heuristic algorithms; Directed graphs; COSTS; systematics; Prediction algorithms; dynamic networks; identification; optimal observations

摘要

This article presents a systematic algorithm to select a set of auxiliary measurements in order to consistently identify certain transfer functions in a dynamic network from observational data. The selection of the auxiliary measurements is obtained by minimizing an appropriate cost function. It is assumed that the topology of the network is known, the forcing inputs are not measured, and the observations have positive additive costs. It is shown that sufficient and necessary conditions for consistent identification of a single transfer function based on a multi-input single-output prediction error method, are equivalent to the notion of minimum cut in an augmented graph resulted from systematically manipulating the graphical representation of the network. Then, the optimal set of auxiliary measurements minimizing the cost could be found using different approaches such as algorithms from graph theory (i.e., Ford-Fulkerson), distributed algorithms (i.e., push-relabel algorithm), or purely optimization based procedures (i.e., linear programming). The results are also extended to the more challenging scenario, where the objective is simultaneously identifying multiple transfer functions.