Resistive-capacitive (RC) networks are used to model various processes in engineering, physics or biology. We consider the problem of recovering the network connection structure from measured input-output data. We address this problem as a structured identification one, that is, we assume to have a state-space model of the system (identified with standard techniques, such as subspace methods) and find a coordinate transformation that puts the identified system in a form that reveals the nodes connection structure. We characterize the solution set, that is, the set of all possible RC-networks that can be associated to the input-output data. We present a possible solution algorithm and show some computational experiments.
@article{arxiv.2007.10046,
title = {Structured identification for network reconstruction of RC-models},
author = {Gabriele Calzavara and Luca Consolini and Juxhino Kavaja},
journal= {arXiv preprint arXiv:2007.10046},
year = {2020}
}