Journal ArticleDOI
An iterative method for the identification of nonlinear systems using a Hammerstein model
Kumpati S. Narendra,P. Gallman +1 more
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TLDR
In this article, an iterative method is proposed for the identification of nonlinear systems from samples of inputs and outputs in the presence of noise, which consists of a no-memory gain (of an assumed polynomial form) followed by a linear discrete system.Abstract:
An iterative method is proposed for the identification of nonlinear systems from samples of inputs and outputs in the presence of noise. The model used for the identification consists of a no-memory gain (of an assumed polynomial form) followed by a linear discrete system. The parameters of the pulse transfer function of the linear system and the coefficients of the polynomial non-linearity are alternately adjusted to minimize a mean square error criterion. Digital computer simulations are included to demonstrate the feasibility of the technique.read more
Citations
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Journal ArticleDOI
Non-parametric identification of non-linearity in hammerstein systems
TL;DR: It is shown that the algorithms converge to the non-linearity with growing number of measurements n and attain the convergence rate O (n -2p/(2p+1) ) - the best possible for non-parametric algorithms.
Proceedings ArticleDOI
A Multiband Structure based on Hammerstein Model for Nonlinear Audio System Identification
TL;DR: The proposed work aims at showing two innovative approaches based on multiband structure adaptive filters for Hammerstein adaptive identification process using the discrete cosine transform and the wavelet transform for the filterbank construction.
Journal ArticleDOI
Robustness analysis of Wiener systems
TL;DR: In this article, the robustness of a typical control scheme is analyzed using a parametric description for the Wiener system, which allows to describe the uncertainty as a set of parameters for the linear and the nonlinear blocks.
Book ChapterDOI
On the Convergence of Iterative Identification of Hammerstein Systems
TL;DR: In this article, the authors proposed a new algorithm by fixing the norm of the parameter estimates, which ensures the convergence property under arbitrary nonzero initial conditions, and the proofs of the property give a geometrical explanation on why the normalization guarantees the convergence.
Proceedings ArticleDOI
On Identification of Discrete Multivariate Hammerstein System by Kernel Regression Estimate
TL;DR: In this paper, the impulse response function of dynamical, linear subsystem is obtained by the correlation method and its local as well as global convergence is shown as the number of measurements tends to infinity.
References
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Journal ArticleDOI
A technique for the identification of linear systems
Kenneth Steiglitz,L. McBride +1 more
TL;DR: In this paper, an iterative technique is proposed to identify a linear system from samples of its input and output in the presence of noise by minimizing the mean-square error between system and model outputs.
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.