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.
Structured identification for network reconstruction of RC-models / Calzavara, G.; Consolini, L.; Kavaja, J.. - In: SYSTEMS & CONTROL LETTERS. - ISSN 0167-6911. - 147:(2021), p. 104849.104849. [10.1016/j.sysconle.2020.104849]
Structured identification for network reconstruction of RC-models
Calzavara G.;Consolini L.
;Kavaja J.
2021-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
