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• W2091116596 abstract "- This paper analyses the variance of linear models that are identified in the presence of nonlinear distortions. The best linear approximation GBLA to a nonlinear system, driven by a Gaussian distributed random input, is identified in least squares sense. Even in the absence of disturbing noise, the estimated model ĜBLA will vary from one realization of the excitation signal to the other. In this paper it will be shown that the variance of the nonparametric frequency response function (FRF) ĜBLA(k) can still be obtained from the experimental data using the classical variance formulas. However, for parametric estimates ĜBLA(q, θ), an increased variance is observed. The contributing terms to this additional variance are studied in this paper." @default.
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• W2091116596 date "2009-01-01" @default.
• W2091116596 modified "2023-09-24" @default.
• W2091116596 title "ANALYSIS OF THE NONLINEAR INDUCED VARIANCE IN LINEAR SYSTEM IDENTIFICATION" @default.
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• W2091116596 doi "https://doi.org/10.3182/20090706-3-fr-2004.00105" @default.
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