A Least Squares Interpretation of Sub-Space Methods for System Identification
1996 (English)In: Proceedings of the 35th IEEE Conference on Decision and Control, 1996, 335-342 vol.1 p.Conference paper (Refereed)
So called subspace methods for direct identification of linear models in state space form have drawn considerable interest. The algorithms consist of series of quite complex projections, and it is not so easy to intuitively understand how they work. They have also defied, so far, complete asymptotic analysis of their stochastic properties. This contribution describes an interpretation of how they work. It specifically deals with how consistent estimates of the dynamics can be achieved, even though correct predictors are not used. We stress how the basic idea is to focus on the estimation of the state-variable candidates-the k-step ahead output predictors.
Place, publisher, year, edition, pages
1996. 335-342 vol.1 p.
Subspace methods, Parameter estimation, Prediction theory, Least-squares approximations
IdentifiersURN: urn:nbn:se:liu:diva-93768DOI: 10.1109/CDC.1996.574330ISBN: 0-7803-3590-2OAI: oai:DiVA.org:liu-93768DiVA: diva2:628935
35th IEEE Conference on Decision and Control, Kobe, Japan, December, 1996