Interpretation of Subspace Methods: Consistency Analysis
1997 (English)In: Proceedings of the 11th IFAC Symposium on System Identification, 1997, Vol. 3, 1125-1130 p.Conference paper (Refereed)
So called subspace methods for direct identification of linear state space models form a very useful alternative to maximum-likelihood type approaches, inthat they are non-iterative and offer efficient numerical implementations. The algorithms consist of series of quite complex projections, and it is not so easy to intuitively understand how they work. The asymptotic analysis of them is also complicated. This contribution describes an interpretation of how they work in terms of k-step ahead predictors of carefully chosen orders. It specifically deals how consistent estimates of the dynamics can be achieved, even though correct predictors are not used. This analysis gives some new angles of attack to the problem of asymptotic behavior ofthe subspace algorithms.
Place, publisher, year, edition, pages
1997. Vol. 3, 1125-1130 p.
Identification algorithms, Consistency, Least squares, Pedictors
IdentifiersURN: urn:nbn:se:liu:diva-93800ISBN: 0080425925OAI: oai:DiVA.org:liu-93800DiVA: diva2:626767
11th IFAC Symposium on System Identification, Fukuoka, Japan, July, 1997
FunderSwedish Research Council