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Journal ArticleDOI

An iterative method for the identification of nonlinear systems using a Hammerstein model

Kumpati S. Narendra, +1 more
- 01 Jul 1966 - 
- Vol. 11, Iss: 3, pp 546-550
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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.

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Citations
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Journal ArticleDOI

Relay based identification of Hammerstein model

TL;DR: In this article, a Hammerstein system with a more generalized linear subsystem is identified using a hysteretic relay in feedback using a polynomial curve fitting technique to recover the output signal from measurement noise.
Journal ArticleDOI

A Correlation Least-Squares Method for Hammerstein Model Identification with ARX and μ-Markov Structures

TL;DR: In this paper, a two-step method for identifying the SISO Hammerstein model is presented, which employs input autocorrelation and input-output cross-correlation functions as data for least-squares estimation.
Proceedings ArticleDOI

Structured experimental modeling of complex nonlinear systems

TL;DR: In this article, an iterative algorithm is proposed for the identification of a system composed of two MIMO systems, one linear and the other one nonlinear, interconnected by an unknown multivariable signal.

Nonlinear Identification of Wavenet Based Hammerstein Model - Case Study on High Purity Distillation Column

TL;DR: In this article, a nonlinear static element described by the Hammerstein model is used to represent the no linearity of the chemical processes in high purity distillation and the parameters of the parameters are identified using iterative prediction-error minimization method.
Journal ArticleDOI

Hammerstein Box-Jenkins System Identification of the Cascaded Tanks Benchmark System

TL;DR: A nonlinear system identification approach is presented in this work to predict the model structure parameters that minimize the difference between the estimated and measured data, using benchmark datasets.
References
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Journal ArticleDOI

A technique for the identification of linear systems

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.
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