Structured identification for network reconstruction of RC-models

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...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Calzavara, Gabriele, Consolini, Luca, Kavaja, Juxhino
Format: Article
Language:English
Subjects:
Algorithms
Coordinate transformations
Identification methods
State space models
Subspace methods
Online Access:Get full text
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Summary: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.
ISSN:2331-8422