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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Identification of Polynomic Systems-A Short Review
TL;DR: Early in the twentieth century, Frechet as mentioned in this paper showed that a large class of nonlinear time invariant systems can be represented by the functional series and showed that functional series can be used to represent time invariants.
Proceedings ArticleDOI
Hybrid Identification of L-N-L and N-L-N Systems
TL;DR: In the paper, the idea of combined (parametric-non parametric) approach to nonlinear system identification is shown and the consistency of the particular estimates, in the presence of random noise, is proved.
Using cascaded neural net filter in non-linear identification of acoustic echo path
Lester S.H. Ngia,Jonas Sjöberg +1 more
TL;DR: A cascaded neural net filter is used as the filter structure in a non-linear AEC that converges to a higher echo return loss enhancement (ERLE) than the linear adaptive FIR filter.
Review Selecting nonlinear model structures for computer control
TL;DR: In this paper, the authors describe some broad classes of nonlinear model structures, which may be approximately characterized as mildly nonlinear, strongly nonlinear or of intermediate nonlinearity, depending on the different ways they violate linear intuition.
Proceedings ArticleDOI
A low-complexity memoryless model for envelope tracking RF power amplifiers
TL;DR: A new kind of behavioral model structure for characterizing static nonlinearities in dynamically supplied RF power amplifiers is presented to simplify the real-time design and implementation of adaptive shaping functions devoted to improve the efficiency in systems controlled by digital signal processors with tight computational capabilities.
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.