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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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
Integration of Multilayer Perceptron Networks and Linear Dynamic Models
Hong-Te Su,Thomas J. McAvoy +1 more
TL;DR: A complex polymerization process is used to demonstrate the proposed Neural Network Hammerstein (NNH) modeling approach in order to fully utilize the abundant steady state information.
Iterative identification of Wiener model using hysteresis memory-less nonlinearity.
TL;DR: In this paper, the decomposition of the two switched saturation preposition is applied to pseudo linear regression to identify the saturation with hysteresis model in the Wiener model and the identification procedure applies the least square algorithm.
Proceedings ArticleDOI
Identification of Hammerstein models for control
TL;DR: Identification of single-input single-output Hammerstein models is studied and a relaxation iteration scheme is proposed by making use of a model structure in which the error is bilinear in the parameters.
Journal ArticleDOI
Structure and parameter identification for Bayesian Hammerstein system
TL;DR: In this paper, a structure and parameter identification problem for Bayesian Hammerstein system is considered, in which the system order, system parameters and regularization parameters are all unknown in the considered system.
Journal ArticleDOI
Structured SM identification of vehicles vertical dynamics
TL;DR: In this paper, the problem of identifying discrete time nonlinear systems in regression form from finite and noise corrupted measurements is considered, where information about the physical structure of the system to be identified, this can be decomposed into interacting subsystems.
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.