Journal ArticleDOI
An iterative method for the identification of nonlinear systems using a Hammerstein model
Kumpati S. Narendra,P. Gallman +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review
Journal ArticleDOI
Convergence of fixed-point iteration for the identification of Hammerstein and Wiener systems
Guoqi Li,Changyun Wen +1 more
TL;DR: In this article, a fixed-point iteration is introduced to identify both Hammerstein and Wiener systems with a unified algorithm, and the errors of the estimates are established as functions of the noise variance, and how the noise affects the quality of parameter estimates for a finite number of data points is made clear.
Journal ArticleDOI
Identification and Control of a Fuel Cell System in the Presence of Time-Varying Disturbances
Wei Wu,Hou Tsen Chen +1 more
TL;DR: A time-Varying Hammerstein model is developed to identify a fuel cell system in the presence of time-varying disturbances since the load variations are usually time- varying and unknown.
Journal ArticleDOI
Nonlinear System Identification Using Pseudorandom Signals with Partially Orthogonal Transforms
H.A. Barker,M.H. Ai-Hilal +1 more
TL;DR: It is shown that appropriate choice of pseudorandom input signal levels not only allows complete decoupling of odd-and evenorder nonlinear effects on the system output transform, but also enables first-and third-order non linear effects onThe system output transforms to be sufficiently decoupled to allow their identification to be accomplished.
Proceedings Article
Nonlinear Black-Box Structures -- Some Approaches and Some Examples
TL;DR: Many possible model structures between linear models and the fully nonlinear models are discussed and a "natural" way of how to extend the identiication procedure from linear to non linear models is given.
References
More filters
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.