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
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Book ChapterDOI
Modeling of Distributed Parameter Systems: Overview and Classification
Han-Xiong Li,Chenkun Qi +1 more
TL;DR: This chapter provides a systematic overview of the distributed parameter system (DPS) modeling and its classification, which includes model reduction for known DPS, parameter estimation for DPS, and system identification for unknown DPS.
Proceedings ArticleDOI
An identification approach of Hammerstein model
TL;DR: In order to enhance the precision and stability of the identification algorithm, a modified particle swarm optimization (MPSO) algorithm is applied to search the parameter space to find the optimal parametric estimation values of the model.
Book ChapterDOI
Use of Swarm Intelligence for the Identification of a Class of Nonlinear Dynamical Systems
TL;DR: Property of swarm intelligence are exploited to solve the identification problem of a class of nonlinear dynamical systems known as Hammerstein systems, and the presented identification technique provides encouraging estimation results.
Identification of linear systems with delay via a learning model
TL;DR: In this article, the effects of input delay on an identification scheme using a learning model are investigated and the parameter adjustment laws for the learning model were derived through Lyapunov methods similar to those used for the model reference adaptive control systems of Parks.
Book ChapterDOI
A New Networked Identification Approach for a Class of Hammerstein Systems
TL;DR: In this paper, an iterative identification method is implemented over a physical IEEE 802.11b wireless channel, where the identified model is used into next identification process to produce the estimated values of the plant outputs for compensating the influence of network delays.
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.