Modeling complex systems by generalized factor analysis

G. Bottegal, G Picci

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

5 Citaten (Scopus)


We propose a new modeling paradigm for large dimensional aggregates of stochastic systems by Generalized Factor Analysis (GFA) models. These models describe the data as the sum of a flocking plus an uncorrelated idiosyncratic component. The flocking component describes a sort of collective orderly motion which admits a much simpler mathematical description than the whole ensemble while the idiosyncratic component describes weakly correlated noise. We first discuss static GFA representations and characterize in a rigorous way the properties of the two components. The extraction of the dynamic flocking component is discussed for time-stationary linear systems and for a simple classes of separable random fields.
Originele taal-2Engels
Pagina's (van-tot)759-774
Aantal pagina's16
TijdschriftIEEE Transactions on Automatic Control
Nummer van het tijdschrift3
StatusGepubliceerd - 2015
Extern gepubliceerdJa


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