Structured identification for network reconstruction of RC-models

TL;DR

This paper presents a method for reconstructing the connection structure of RC networks from input-output data by transforming identified state-space models into a form that reveals network topology.

Contribution

It introduces a structured identification approach that transforms identified models to uncover network connections, providing a new way to analyze RC-models from data.

Findings

Characterizes the set of all possible RC-networks consistent with data

Proposes a solution algorithm for network reconstruction

Demonstrates computational experiments validating the approach

Abstract

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.