Identification of Nonlinear Model with General Disturbances

Wang Xiaoping; Yao Jie; Wang Jianhong; Liu Feifei

doi:doi:10.11648/j.iotcc.20180601.13

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Identification of Nonlinear Model with General Disturbances

Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei

Published in Internet of Things and Cloud Computing (Volume 6, Issue 1)

Received: 5 February 2018 Accepted: 25 February 2018 Published: 26 March 2018

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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.

Published in	Internet of Things and Cloud Computing (Volume 6, Issue 1)
DOI	10.11648/j.iotcc.20180601.13
Page(s)	17-24
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

System Identification, Nonlinear System, Bussgang’s Theorem, Maximum Likelihood Method

References

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[5]	G Calafiore, M C Campi. “Uncertain convex programs: randomized solutions and confidence levels,” Mathematical Programming, vol 102, no 11, pp. 25-46, 2005.
[6]	M Milanese, C Novara. “Set membership identification of nonlinear systems,” Automatica, vol 40, no 6, pp. 957-975, 2004.
[7]	T Alamo, J M Bravo, E F Camacho. “Guaranteed state estimation by zonotopes,” Automatica, vol 41, no 6, pp. 1035-1043, 2005.
[8]	J M Bravo, T Alamo, E F Camacho. “Bounded error identification of systems with time varying parameters,” IEEE Transaction on Automatic Control, vol 51, no 7, pp. 1144-1150, 2006.
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APA Style

Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei. (2018). Identification of Nonlinear Model with General Disturbances. Internet of Things and Cloud Computing, 6(1), 17-24. https://doi.org/10.11648/j.iotcc.20180601.13

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ACS Style

Wang Xiaoping; Yao Jie; Wang Jianhong; Liu Feifei. Identification of Nonlinear Model with General Disturbances. Internet Things Cloud Comput. 2018, 6(1), 17-24. doi: 10.11648/j.iotcc.20180601.13

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AMA Style

Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei. Identification of Nonlinear Model with General Disturbances. Internet Things Cloud Comput. 2018;6(1):17-24. doi: 10.11648/j.iotcc.20180601.13

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@article{10.11648/j.iotcc.20180601.13,
  author = {Wang Xiaoping and Yao Jie and Wang Jianhong and Liu Feifei},
  title = {Identification of Nonlinear Model with General Disturbances},
  journal = {Internet of Things and Cloud Computing},
  volume = {6},
  number = {1},
  pages = {17-24},
  doi = {10.11648/j.iotcc.20180601.13},
  url = {https://doi.org/10.11648/j.iotcc.20180601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20180601.13},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Identification of Nonlinear Model with General Disturbances
AU  - Wang Xiaoping
AU  - Yao Jie
AU  - Wang Jianhong
AU  - Liu Feifei
Y1  - 2018/03/26
PY  - 2018
N1  - https://doi.org/10.11648/j.iotcc.20180601.13
DO  - 10.11648/j.iotcc.20180601.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 17
EP  - 24
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20180601.13
AB  - 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.
VL  - 6
IS  - 1
ER  -

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Author Information

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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Plain Text BibTeX RIS

APA Style

Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei. (2018). Identification of Nonlinear Model with General Disturbances. Internet of Things and Cloud Computing, 6(1), 17-24. https://doi.org/10.11648/j.iotcc.20180601.13

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ACS Style

Wang Xiaoping; Yao Jie; Wang Jianhong; Liu Feifei. Identification of Nonlinear Model with General Disturbances. Internet Things Cloud Comput. 2018, 6(1), 17-24. doi: 10.11648/j.iotcc.20180601.13

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AMA Style

Wang Xiaoping, Yao Jie, Wang Jianhong, Liu Feifei. Identification of Nonlinear Model with General Disturbances. Internet Things Cloud Comput. 2018;6(1):17-24. doi: 10.11648/j.iotcc.20180601.13

Copy | Download

@article{10.11648/j.iotcc.20180601.13,
  author = {Wang Xiaoping and Yao Jie and Wang Jianhong and Liu Feifei},
  title = {Identification of Nonlinear Model with General Disturbances},
  journal = {Internet of Things and Cloud Computing},
  volume = {6},
  number = {1},
  pages = {17-24},
  doi = {10.11648/j.iotcc.20180601.13},
  url = {https://doi.org/10.11648/j.iotcc.20180601.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20180601.13},
  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.},
 year = {2018}
}

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TY  - JOUR
T1  - Identification of Nonlinear Model with General Disturbances
AU  - Wang Xiaoping
AU  - Yao Jie
AU  - Wang Jianhong
AU  - Liu Feifei
Y1  - 2018/03/26
PY  - 2018
N1  - https://doi.org/10.11648/j.iotcc.20180601.13
DO  - 10.11648/j.iotcc.20180601.13
T2  - Internet of Things and Cloud Computing
JF  - Internet of Things and Cloud Computing
JO  - Internet of Things and Cloud Computing
SP  - 17
EP  - 24
PB  - Science Publishing Group
SN  - 2376-7731
UR  - https://doi.org/10.11648/j.iotcc.20180601.13
AB  - 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.
VL  - 6
IS  - 1
ER  -

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