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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Proceedings ArticleDOI
Study on identification algorithm of a class of nonlinear model
TL;DR: A parameter identification method of Hammerstein model with two-segment piecewise nonlinearities with improved particle swarm optimization (IPSO) algorithm is studied to solve the optimization problem of the nonlinear system identification.
Block-oriented Nonlinear System Identification Using Semidefinite Programming
TL;DR: The research work presented in this dissertation proposes a new approach for block-oriented system identification by tackling the inaccessibility of measurement of intermediate signals in block- oriented nonlinear systems via rank minimization.
Proceedings ArticleDOI
On identification of nonstationary Hammerstein systems by the Fourier series regression estimate
TL;DR: In this article, a single-input, single-output (SISO) discrete Hammerstein system is identified, which consists of a nonlinear, memoryless subsystem followed by a dynamic, linear subsystem.
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.