Nonlinear system identification based on NARX network

Hongwei Liu; Xiaodong Song

doi:10.1109/ASCC.2015.7244449

Nonlinear system identification based on NARX network

Hongwei Liu, Xiaodong Song

School of Aerospace Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Citations (Scopus)

Abstract

This paper discusses identification of nonlinear system with nonlinear AutoRegressive models with eXogenous inputs (NARX). NARX network is a dynamic neural network which appears effective in the input-output identification of both linear and nonlinear systems. When identifying them by NARX model, the first step is to collect training data and the final results vary considerably with different training data. The paper compares the training results of three kinds of signals, including SPHS signal, Gaussian white noise and mixed signal. Our results show the response characteristics of NARX model trained by different signals can be used to design the input training signal.

Original language	English
Title of host publication	2015 10th Asian Control Conference
Subtitle of host publication	Emerging Control Techniques for a Sustainable World, ASCC 2015
Editors	Hazlina Selamat, Hafiz Rashidi Haruna Ramli, Ahmad Athif Mohd Faudzi, Ribhan Zafira Abdul Rahman, Asnor Juraiza Ishak, Azura Che Soh, Siti Anom Ahmad
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479978625
DOIs	https://doi.org/10.1109/ASCC.2015.7244449
Publication status	Published - 8 Sept 2015
Event	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia Duration: 31 May 2015 → 3 Jun 2015

Publication series

Name	2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015

Conference

Conference	10th Asian Control Conference, ASCC 2015
Country/Territory	Malaysia
City	Kota Kinabalu
Period	31/05/15 → 3/06/15

Keywords

Gaussian white noise
NARX network
SPHS signal
system identification

Access to Document

10.1109/ASCC.2015.7244449

Cite this

Liu, H., & Song, X. (2015). Nonlinear system identification based on NARX network. In H. Selamat, H. R. H. Ramli, A. A. M. Faudzi, R. Z. A. Rahman, A. J. Ishak, A. C. Soh, & S. A. Ahmad (Eds.), 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 Article 7244449 (2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASCC.2015.7244449

Liu, Hongwei ; Song, Xiaodong. / Nonlinear system identification based on NARX network. 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. editor / Hazlina Selamat ; Hafiz Rashidi Haruna Ramli ; Ahmad Athif Mohd Faudzi ; Ribhan Zafira Abdul Rahman ; Asnor Juraiza Ishak ; Azura Che Soh ; Siti Anom Ahmad. Institute of Electrical and Electronics Engineers Inc., 2015. (2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015).

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title = "Nonlinear system identification based on NARX network",

abstract = "This paper discusses identification of nonlinear system with nonlinear AutoRegressive models with eXogenous inputs (NARX). NARX network is a dynamic neural network which appears effective in the input-output identification of both linear and nonlinear systems. When identifying them by NARX model, the first step is to collect training data and the final results vary considerably with different training data. The paper compares the training results of three kinds of signals, including SPHS signal, Gaussian white noise and mixed signal. Our results show the response characteristics of NARX model trained by different signals can be used to design the input training signal.",

keywords = "Gaussian white noise, NARX network, SPHS signal, system identification",

author = "Hongwei Liu and Xiaodong Song",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th Asian Control Conference, ASCC 2015 ; Conference date: 31-05-2015 Through 03-06-2015",

year = "2015",

month = sep,

day = "8",

doi = "10.1109/ASCC.2015.7244449",

language = "English",

series = "2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Hazlina Selamat and Ramli, {Hafiz Rashidi Haruna} and Faudzi, {Ahmad Athif Mohd} and Rahman, {Ribhan Zafira Abdul} and Ishak, {Asnor Juraiza} and Soh, {Azura Che} and Ahmad, {Siti Anom}",

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Liu, H & Song, X 2015, Nonlinear system identification based on NARX network. in H Selamat, HRH Ramli, AAM Faudzi, RZA Rahman, AJ Ishak, AC Soh & SA Ahmad (eds), 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015., 7244449, 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015, Institute of Electrical and Electronics Engineers Inc., 10th Asian Control Conference, ASCC 2015, Kota Kinabalu, Malaysia, 31/05/15. https://doi.org/10.1109/ASCC.2015.7244449

Nonlinear system identification based on NARX network. / Liu, Hongwei; Song, Xiaodong.
2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. ed. / Hazlina Selamat; Hafiz Rashidi Haruna Ramli; Ahmad Athif Mohd Faudzi; Ribhan Zafira Abdul Rahman; Asnor Juraiza Ishak; Azura Che Soh; Siti Anom Ahmad. Institute of Electrical and Electronics Engineers Inc., 2015. 7244449 (2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Nonlinear system identification based on NARX network

AU - Liu, Hongwei

AU - Song, Xiaodong

PY - 2015/9/8

Y1 - 2015/9/8

N2 - This paper discusses identification of nonlinear system with nonlinear AutoRegressive models with eXogenous inputs (NARX). NARX network is a dynamic neural network which appears effective in the input-output identification of both linear and nonlinear systems. When identifying them by NARX model, the first step is to collect training data and the final results vary considerably with different training data. The paper compares the training results of three kinds of signals, including SPHS signal, Gaussian white noise and mixed signal. Our results show the response characteristics of NARX model trained by different signals can be used to design the input training signal.

AB - This paper discusses identification of nonlinear system with nonlinear AutoRegressive models with eXogenous inputs (NARX). NARX network is a dynamic neural network which appears effective in the input-output identification of both linear and nonlinear systems. When identifying them by NARX model, the first step is to collect training data and the final results vary considerably with different training data. The paper compares the training results of three kinds of signals, including SPHS signal, Gaussian white noise and mixed signal. Our results show the response characteristics of NARX model trained by different signals can be used to design the input training signal.

KW - Gaussian white noise

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KW - system identification

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BT - 2015 10th Asian Control Conference

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A2 - Rahman, Ribhan Zafira Abdul

A2 - Ishak, Asnor Juraiza

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 31 May 2015 through 3 June 2015

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Liu H, Song X. Nonlinear system identification based on NARX network. In Selamat H, Ramli HRH, Faudzi AAM, Rahman RZA, Ishak AJ, Soh AC, Ahmad SA, editors, 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7244449. (2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015). doi: 10.1109/ASCC.2015.7244449

Nonlinear system identification based on NARX network

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