Topological properties in identification and modeling techniques

Giacomo Innocenti, Donatello Materassi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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 languageEnglish (US)
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition1 PART 1
StatePublished - 2008
Externally publishedYes
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: Jul 6 2008Jul 11 2008

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
ISSN (Print)1474-6670


Other17th World Congress, International Federation of Automatic Control, IFAC
Country/TerritoryKorea, Republic of

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”.


  • Estimation and filtering
  • Frequency domain identification
  • Stochastic system identification


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