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
Consistent parameter estimation and convergence properties analysis of hammerstein output-error models
Bi Zhang,Zhizhong Mao +1 more
TL;DR: This paper presents an on-line bias-compensating recursive least squares (BCRLS) identification algorithm for Hammerstein output-error models disturbed by non-martingale difference sequence noise that achieves consistent parameter estimation without the strictly positive real (SPR) condition.
Journal ArticleDOI
Nonlinear system identification under various prior knowledge
TL;DR: Several algorithms for nonlinear system identification exploit various degrees of prior knowledge - from parametric - to nonparametric, until a semiparametric algorithm, which shares advantages of both approaches is announced.
Proceedings ArticleDOI
Recursive Identification of Dynamic Systems with Time-Varying Memoryless Nonlinearities having Deadbands
Po-Chuan Chow,Howard J. Chizeck +1 more
TL;DR: An algorithm has been developed for the recursive identification of nonlinear systems from input and output measurement data by representing the nonlinearity as a shifted polynomial, where the shift (along the input axis) models the deadband.
Proceedings ArticleDOI
Nonlinear convolution and fourier series coefficients estimate
TL;DR: An original description of the procedure for the modeling of nonlinear systems based on the socalled non linear convolution approach is reported and a Fourier Series implementation of the model allows a significant reduction in the associated computational cost.
Journal ArticleDOI
Identification of wiener-hammerstein models with cubic nonlinearity using lifred
Ai Hui Tan,Keith R. Godfrey +1 more
TL;DR: In this paper, the identification of Wiener-Hammerstein models using linear interpolation in the frequency domain (LIFRED) is extended from models with quadratic nonlinearity to models with cubic non-linearity.
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.