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

宇航学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

19 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	2015 10th Asian Control Conference
主期刊副标题	Emerging Control Techniques for a Sustainable World, ASCC 2015
编辑	Hazlina Selamat, Hafiz Rashidi Haruna Ramli, Ahmad Athif Mohd Faudzi, Ribhan Zafira Abdul Rahman, Asnor Juraiza Ishak, Azura Che Soh, Siti Anom Ahmad
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781479978625
DOI	https://doi.org/10.1109/ASCC.2015.7244449
出版状态	已出版 - 8 9月 2015
活动	10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, 马来西亚期限: 31 5月 2015 → 3 6月 2015

出版系列

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

会议

会议	10th Asian Control Conference, ASCC 2015
国家/地区	马来西亚
市	Kota Kinabalu
时期	31/05/15 → 3/06/15

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10.1109/ASCC.2015.7244449

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引用此

Liu, H., & Song, X. (2015). Nonlinear system identification based on NARX network. 在 H. Selamat, H. R. H. Ramli, A. A. M. Faudzi, R. Z. A. Rahman, A. J. Ishak, A. C. Soh, & S. A. Ahmad (编辑), 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 文章 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. 编辑 / 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).

@inproceedings{71d95c962d634c16a19bd43d2118d2bf,

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}",

booktitle = "2015 10th Asian Control Conference",

address = "United States",

}

Liu, H & Song, X 2015, Nonlinear system identification based on NARX network. 在 H Selamat, HRH Ramli, AAM Faudzi, RZA Rahman, AJ Ishak, AC Soh & SA Ahmad (编辑), 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, 马来西亚, 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. 编辑 / 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).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

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

KW - NARX network

KW - SPHS signal

KW - system identification

UR - http://www.scopus.com/inward/record.url?scp=84957694287&partnerID=8YFLogxK

U2 - 10.1109/ASCC.2015.7244449

DO - 10.1109/ASCC.2015.7244449

M3 - Conference contribution

AN - SCOPUS:84957694287

T3 - 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015

BT - 2015 10th Asian Control Conference

A2 - Selamat, Hazlina

A2 - Ramli, Hafiz Rashidi Haruna

A2 - Faudzi, Ahmad Athif Mohd

A2 - Rahman, Ribhan Zafira Abdul

A2 - Ishak, Asnor Juraiza

A2 - Soh, Azura Che

A2 - Ahmad, Siti Anom

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th Asian Control Conference, ASCC 2015

Y2 - 31 May 2015 through 3 June 2015

ER -

Liu H, Song X. Nonlinear system identification based on NARX network. 在 Selamat H, Ramli HRH, Faudzi AAM, Rahman RZA, Ishak AJ, Soh AC, Ahmad SA, 编辑, 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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