Modeling Complex Systems by Generalized Factor Analysis
2015 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 60, no 3, 759-774 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2015. Vol. 60, no 3, 759-774 p.
Collective behavior, complex systems, flocking, generalized factor analysis, multi-agent systems, stochastic systems
IdentifiersURN: urn:nbn:se:kth:diva-163457DOI: 10.1109/TAC.2014.2357913ISI: 000350206000012ScopusID: 2-s2.0-84923583763OAI: oai:DiVA.org:kth-163457DiVA: diva2:801034
FunderSwedish Research Council, 621-2009-4017
QC 201504082015-04-082015-04-072015-04-08Bibliographically approved