The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
1993 (English)Report (Other academic)
The least-squares identification of FIR systems is analyzed assuming that the noise is a bounded signal and the input signal is a pseudo-random binary sequence. A lower bound on the worst-case transfer function error shows that the lest-square estimate of the transfer function diverges as the order of the FIR system is increased. This implies that, in the presence of the worst-case noise, the trade-off between the estimation error due to the disturbance and the bias error (due to unmodeled dynamics) is significantly different from the corresponding trade-off in the random error case: with a worst-case formulation, the model complexity should not increase indefinitely as the size of the data set increases.
Place, publisher, year, edition, pages
Linköping: Linköping University , 1993. , 13 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1479
Worst-case identification, FIR systems, Leat-squares algorithm, Cybernetik Informationsteori, Maskinelement Servomekanismer Automation
IdentifiersURN: urn:nbn:se:liu:diva-95854ISRN: LiTH-ISY-R-1479OAI: oai:DiVA.org:liu-95854DiVA: diva2:638458