The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
1994 (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 least-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 , 1994. , 13 p.
LiTH-ISY-R, ISSN 1400-3902 ; 1615
Worst-case identification, FIR systems, Least-squares algorithm
Cybernetik Informationsteori, Maskinelement Servomekanismer Automation
IdentifiersURN: urn:nbn:se:liu:diva-55182ISRN: LiTH-ISY-R-1615OAI: oai:DiVA.org:liu-55182DiVA: diva2:315795