The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
1994 (English)In: Proceedings of the 10th IFAC Symposium on System Identification, 1994, Vol. 2, 85-90 p.Conference paper (Refereed)
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
1994. Vol. 2, 85-90 p.
Least-squares identification, FIR system
IdentifiersURN: urn:nbn:se:liu:diva-94125ISBN: 978-0080422251OAI: oai:DiVA.org:liu-94125DiVA: diva2:629309
10th IFAC Symposium on System Identification, Copenhagen, Denmark, July, 1994