Journal ArticleDOI
Frequency domain identification of wiener models
TLDR
In this article, the authors proposed a frequency domain approach and showed its convergence for both the linear and non-linear subsystems in the presence of noise, where no a priori knowledge of the structure of the non linearity is required and the linear part can be nonparametric.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2003-09-01. It has received 62 citations till now. The article focuses on the topics: Wiener deconvolution & Frequency domain.read more
Citations
More filters
Journal ArticleDOI
Identification of Hammerstein nonlinear ARMAX systems
Feng Ding,Tongwen Chen +1 more
TL;DR: Two identification algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates.
Journal ArticleDOI
Convergence of the iterative Hammerstein system identification algorithm
Er-Wei Bai,Duan Li +1 more
TL;DR: It is shown that the iterative algorithm with normalization is convergent in general and takes place in one step (two least squares iterations) for FIR Hammerstein models with i.i.d. inputs.
Journal ArticleDOI
Maximum likelihood identification of Wiener models
TL;DR: Comparisons between this new algorithm and the traditional approach, confirm that the new method is unbiased and also has superior accuracy, and derive the Maximum Likelihood method.
Journal ArticleDOI
Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties
TL;DR: A novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented, used for the identification of Hammerstein-type nonlinear systems.
Journal ArticleDOI
Novel Cascade Spline Architectures for the Identification of Nonlinear Systems
TL;DR: Two novel nonlinear cascade adaptive architectures suitable for the identification of general nonlinear systems are presented and a simple form of the on-line adaptation algorithms for the two architectures is derived.
References
More filters
Journal ArticleDOI
Complex-curve fitting
TL;DR: In this paper, a method of evaluation of the polynomial coefficients is presented based on the minimization of the weighted sum of the squares of the errors between the absolute magnitudes of the actual function and the Polynomial ratio, taken at various values of frequency (the independent variable).
Journal ArticleDOI
A blind approach to the Hammerstein-Wiener model identification
TL;DR: By using the blind approach, it is shown that all internal variables can be recovered solely based on the output measurements and identification of linear and nonlinear parts can be carried out.
Journal Article
A Blind Approach to the Hammerstein-Wiener Model Identification
TL;DR: In this paper, a blind approach to the sampled Hammerstein-Wiener model identification is proposed, where no a priori structural knowledge about the input nonlinearity is assumed and no white noise assumption is imposed on the input.
Journal ArticleDOI
On the Identification Problem
TL;DR: In this paper, the identification of zero-memory multipoles and two-poles of class n_1 was studied, where the test signals are sine waves of different amplitudes and frequencies, and the measured quanity is the describing function of the device.
Book ChapterDOI
Frequency domain identification of Hammerstein models
TL;DR: In this article, the Hammerstein model identification in the frequency domain using the sampled input-output data is discussed, where the fundamental frequency and harmonics generated by the unknown nonlinearity are explored and a frequency domain approach is proposed.