Identification of Nonlinear Model with General Disturbances
Internet of Things and Cloud Computing
Volume 6, Issue 1, March 2018, Pages: 17-24
Received: Feb. 5, 2018; Accepted: Feb. 25, 2018; Published: Mar. 26, 2018
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Authors
Wang Xiaoping, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
Yao Jie, School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
Wang Jianhong, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
Liu Feifei, School of Electronic Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou, China
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Abstract
The nonlinear model has a linear dynamic system following some static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. For the special case of Gaussian input signals, we estimate the linear part of the Hammerstein model using the Bussgang’s classic theorem. For the case with general disturbances, we derive the Maximum Likelihood method. Finally one simulation example is used to prove the efficiency of our theory.
Keywords
System Identification, Nonlinear System, Bussgang’s Theorem, Maximum Likelihood Method
To cite this article
Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei, Identification of Nonlinear Model with General Disturbances, Internet of Things and Cloud Computing. Vol. 6, No. 1, 2018, pp. 17-24. doi: 10.11648/j.iotcc.20180601.13
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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