Thresholding in high order transfer function estimation
1994 (English)In: Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on, 1994, Vol. 4, 3400-3405 p.Conference paper (Refereed)
A problem in prediction error system identification methods is estimation of pole locations. Typically, iterative numerical optimization methods are used. Reliable initial values are then necessary for good results. The parameterization is often done in the coefficients of transfer function polynomials or some canonical form. In this contribution we discuss a couple of issues related to the above problem. First, we study how all-pass systems can be used to generate suitable model structures. This analysis is based on the relation between balanced realizations of all-pass filters and orthonormal basis transfer functions. Next, we investigate the effects of a priori fixed pole locations, such as in Laguerre and Kautz models. One idea is to use very flexible high-order models. However, the corresponding estimation problem has to be regularized in order to reduce the variance errors due to noise. We will discuss how this can be done by using thresholding of the estimated coefficients
Place, publisher, year, edition, pages
1994. Vol. 4, 3400-3405 p.
Kautz models; Laguerre models; all-pass filters; all-pass systems; balanced realizations; high-order transfer function estimation; iterative numerical optimization methods; orthonormal basis transfer functions; pole locations estimation; prediction error system identification methods; reliable initial values; thresholding; transfer function polynomial coefficients; variance error reduction; very flexible high-order models; all-pass filters; filtering theory; identification; iterative methods; optimisation; poles and zeros; prediction theory; stochastic processes; transfer functions;
IdentifiersURN: urn:nbn:se:kth:diva-57961DOI: 10.1109/CDC.1994.411670OAI: oai:DiVA.org:kth-57961DiVA: diva2:472770
NR 201408052012-01-042012-01-042013-09-05Bibliographically approved