On the convergence of the Prediction Error Method to its global minimum
2012 (English)In: 16th IFAC Symposium on System Identification, IFAC , 2012, 698-703 p.Conference paper (Refereed)
The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The existence of local minima, and hence the difficulty in solving the optimization, depends mainly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. It is therefore possible to avoid the existence of local minima by properly choosing the spectrum of the input; in this paper we show how to perform this choice. We present sufficient conditions for the convergence of PEM to the global minimum and from these conditions we derive two approaches to avoid the existence of nonglobal minima. We present the application of one of these two approaches to a case study where standard identification toolboxes tend to get trapped in nonglobal minima.
Place, publisher, year, edition, pages
IFAC , 2012. 698-703 p.
, IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 16
Convergence, Gradient methods, Identification
IdentifiersURN: urn:nbn:se:kth:diva-72583DOI: 10.3182/20120711-3-BE-2027.00371ScopusID: 2-s2.0-84867061419ISBN: 978-390282306-9OAI: oai:DiVA.org:kth-72583DiVA: diva2:488375
16th IFAC Symposium on System Identification (SYSID 2012), Universite Libre de Bruxelles; Bruxelles; 11 July 2012 through 13 July 2012
FunderICT - The Next Generation
QC 201211222012-02-012012-01-312013-04-15Bibliographically approved