The least-squares identification of FIR systems subject to worst-case noise |
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Authors: | Hüseyin Akay Hkan Hjalmarson |
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Affiliation: | Hüseyin Akçay,Håkan Hjalmarson |
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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 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. |
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Keywords: | Worst-case identification FIR systems least-squares algorithm |
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