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:Â
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
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Dissertation
Data-Rich Multivariable Control of Heavy-Duty Engines
TL;DR: In this article, a neural network model was used to predict exhaust emissions from cylinder pressure data, and a Wasserstein-Schlemper model was trained to predict emissions on a cycle-to-cycle, cylinder-individual basis.
Journal ArticleDOI
Differentiated Performance Management in Virtualized Environments Using Nonlinear Control
TL;DR: A new nonlinear control approach is presented that enables achieving differentiated performance requirements effectively in virtualized environments through the automated provisioning of resources, using a nonlinear block control structure called the Hammerstein and Wiener model.
Journal ArticleDOI
Fault Detection for Nonlinear Dynamic Systems With Consideration of Modeling Errors: A Data-Driven Approach
TL;DR: In this article , a stacked neural network-aided canonical variate analysis (SNNCVA) method is proposed to identify and parameterize nonlinear Hammerstein models using dynamic input and output data, based on which a data-driven residual generator is formed.
Journal ArticleDOI
Identification of a class of non-linear systems with gaussian non-white inputs
TL;DR: It is shown that correlation analysis provides estimates of the parametrized linear and second-order kernels from which the parameters of the linear subsystems can be obtained.
Journal ArticleDOI
Identification of nonlinear multi-degree-of freedom structures based on Hilbert transformation
ZhiGang Wu,Ning Yang,Chao Yang +2 more
TL;DR: In this article, the Hilbert transform identification method was used to identify the nonlinear stiffness of nonlinear hinges in the time domain and several parametric studies were performed to illustrate the feasibility of the methods.
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.