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FRF Measurements Subject to Missing Data: Quantification of Noise, Nonlinear Distortion, and Time-Varying Effects.

, , and . IEEE Trans. Instrumentation and Measurement, 68 (10): 4175-4187 (2019)

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System identification in a real world., , , , , , , , and . AMC, page 1-9. IEEE, (2014)Study of conditional ML estimators in time and frequency-domain system identification., , and . Autom., 35 (1): 91-100 (1999)Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation., , and . Autom., 50 (2): 628-634 (2014)Identification of linear systems with nonlinear distortions., , , and . Autom., 41 (3): 491-504 (2005)Identification of Linear Time-Invariant Systems From Multiple Experiments., and . IEEE Trans. Signal Process., 63 (13): 3549-3554 (2015)The Use of Nonparametric Noise Models Extracted From Overlapping Subrecords for System Identification., , and . IEEE Trans. Signal Process., 59 (10): 4635-4647 (2011)A Gauss-Newton-like optimization algorithm for "weighted" nonlinear least-squares problems., and . IEEE Trans. Signal Process., 44 (9): 2222-2228 (1996)Estimation of affine LPV state space models in the frequency domain: Extension to transient behavior and non-periodic inputs., , and . ECC, page 824-829. IEEE, (2015)Parametric frequency domain identification of a time-varying system as a time-dependent weighted sum of time-invariant systems., and . CDC, page 2029-2034. IEEE, (2010)Continuous time frequency domain LPV state space identification via periodic time-varying input-output modeling., and . CDC, page 2567-2572. IEEE, (2014)