Topological properties in identification and modeling techniques

Giacomo Innocenti; Donatello Materassi

doi:10.3182/20080706-5-KR-1001.3575

Topological properties in identification and modeling techniques

Giacomo Innocenti, Donatello Materassi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

The paper deals with the problem of finding models and predictions within a large set of time series or random processes. Nothing is assumed about their mutual influence and dependence. The problem can not be tackled efficiently, starting from a classical system identification approach. Indeed, the general optimal solution would provide a large number of models, since it would consider every possible interdependence. Then a suboptimal approach will be developed. The proposed technique will present interesting modelling properties which can interpreted in terms of graph theory. The application of this procedure will also be exploited as a tool to provide a clusterization of time series. Finally, we will show that it turns out to be a dynamical generalization of other techniques described in literature.

Original language	English (US)
Title of host publication	Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition	1 PART 1
DOIs	https://doi.org/10.3182/20080706-5-KR-1001.3575
State	Published - 2008
Externally published	Yes
Event	17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of Duration: Jul 6 2008 → Jul 11 2008

Publication series

Name	IFAC Proceedings Volumes (IFAC-PapersOnline)
Number	1 PART 1
Volume	17
ISSN (Print)	1474-6670

Other

Other	17th World Congress, International Federation of Automatic Control, IFAC
Country/Territory	Korea, Republic of
City	Seoul
Period	7/6/08 → 7/11/08

Bibliographical note

Funding Information:
This work has been supported by the Ministero dell’Uni-versità e della Ricerca (MiUR), under the Project PRIN 2005 n. 2005098133 003 “Nonlinear dynamic networks: techniques for robust analysis of deterministic and stochastic models”.

Keywords

Estimation and filtering
Frequency domain identification
Stochastic system identification

Publisher link

10.3182/20080706-5-KR-1001.3575

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Topological properties in identification and modeling techniques. / Innocenti, Giacomo; Materassi, Donatello.
Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1. ed. 2008. (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Innocenti, G & Materassi, D 2008, Topological properties in identification and modeling techniques. in Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC. 1 PART 1 edn, IFAC Proceedings Volumes (IFAC-PapersOnline), no. 1 PART 1, vol. 17, 17th World Congress, International Federation of Automatic Control, IFAC, Seoul, Korea, Republic of, 7/6/08. https://doi.org/10.3182/20080706-5-KR-1001.3575

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N2 - The paper deals with the problem of finding models and predictions within a large set of time series or random processes. Nothing is assumed about their mutual influence and dependence. The problem can not be tackled efficiently, starting from a classical system identification approach. Indeed, the general optimal solution would provide a large number of models, since it would consider every possible interdependence. Then a suboptimal approach will be developed. The proposed technique will present interesting modelling properties which can interpreted in terms of graph theory. The application of this procedure will also be exploited as a tool to provide a clusterization of time series. Finally, we will show that it turns out to be a dynamical generalization of other techniques described in literature.

AB - The paper deals with the problem of finding models and predictions within a large set of time series or random processes. Nothing is assumed about their mutual influence and dependence. The problem can not be tackled efficiently, starting from a classical system identification approach. Indeed, the general optimal solution would provide a large number of models, since it would consider every possible interdependence. Then a suboptimal approach will be developed. The proposed technique will present interesting modelling properties which can interpreted in terms of graph theory. The application of this procedure will also be exploited as a tool to provide a clusterization of time series. Finally, we will show that it turns out to be a dynamical generalization of other techniques described in literature.

KW - Estimation and filtering

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Topological properties in identification and modeling techniques

Abstract

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Keywords

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