Title :
Identification of linear systems in the presence of nonlinear distortions
Author :
Pintelon, R. ; Schoukens, J.
Author_Institution :
Vrije Univ., Brussels, Belgium
Abstract :
This paper treats the identification of linear systems in the presence of nonlinear distortions. It extends the theory developed previously for measurement setups where the input is exactly known and the output is observed with errors (output error framework) to measurement setups where both the input and output are observed with errors (errors-in-variables framework). An appropriate measurement strategy and identification algorithm are presented
Keywords :
Gaussian noise; Volterra series; frequency-domain analysis; identification; linear systems; measurement errors; nonlinear distortion; sampling methods; transfer functions; Gaussian noise; Volterra series; best linear approximation; errors-in-variables framework; frequency domain representation; identification algorithm; linear systems identification; maximum likelihood estimator; measurement setups; measurement strategy; nonlinear distortions; nonlinear operators; output error framework; random phase multisine excitations; related linear dynamic system; sampling rate; stochastic nonlinear contribution; transfer functions; Distortion measurement; Least squares approximation; Linear approximation; Linear systems; Nonlinear distortion; Nonlinear dynamical systems; Nonlinear systems; Phase measurement; Transfer functions; Uninterruptible power systems;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-5890-2
DOI :
10.1109/IMTC.2000.848858
