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
Decoupling the linear and nonlinear parts in Hammerstein model identification
TL;DR: In this article, the linear part of a Hammerstein model is decoupled from the nonlinear part in model identification, and the identification of the linear parts becomes a linear problem and accordingly enjoys the same convergence and consistency results as if the unknown nonlinearity is absent.
Journal ArticleDOI
Frequency domain identification of Wiener models
TL;DR: A frequency domain algorithm for Wiener model identifications based on exploring the fundamental frequency and harmonics generated by the unknown nonlinearity is proposed.
Journal ArticleDOI
Stability Margins of ${\cal L}_{1}$ Adaptive Control Architecture
Chengyu Cao,Naira Hovakimyan +1 more
TL;DR: The L 1 adaptive control architecture for systems in the presence of unknown high-frequency gain with known sign, time-varying unknown parameters and disturbances leads to analytically computable time-delay margin of a semiglobal nature.
Journal ArticleDOI
Nonparametric identification of Hammerstein systems
TL;DR: A discrete-time nonlinear Hammerstein system is identified, and the correlation and frequency-domain methods for identification of its linear subsystem are presented and it is shown that the algorithm convergence to the characteristic of the subsystem regardless of the probability distribution of the input variable.
Journal ArticleDOI
Stochastic approximation in nonparametric identification of Hammerstein systems
TL;DR: Derived from the idea of stochastic approximation, recursive algorithms to identify a Hammerstein system are presented and recover the characteristic of the nonlinear memoryless subsystem, while the third one estimates the impulse response of the linear dynamic part.
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.