Automatica (Journal of IFAC)
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Brief Paper: Study of conditional ML estimators in time and frequency-domain system identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Some peculiarities of identification in the presence of model errors
Automatica (Journal of IFAC)
Technical communique: Initial estimates for the dynamics of a Hammerstein system
Automatica (Journal of IFAC)
Adaptive nonlinear system identification in the short-time fourier transform domain
IEEE Transactions on Signal Processing
Modeling and identification of nonlinear systems in the short-time fourier transform domain
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Nonlinear gray-box identification using local models applied to industrial robots
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Non-parametric estimate of the system function of a time-varying system
Automatica (Journal of IFAC)
Box-Jenkins identification revisited-Part I: Theory
Automatica (Journal of IFAC)
Separability of scalar random multisine signals
Automatica (Journal of IFAC)
Truncation order and its effect in a class of nonlinear systems
Automatica (Journal of IFAC)
Hi-index | 22.17 |
This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.