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- Akçay, Hüseyin, et al.
(författare)
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The Least-Squares Identification of FIR Systems Subject to Worst-Case Noise
- 1993
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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