Identification of ARX hammerstein models based on twin support vector machine regression

Mujahed Aldhaifallah; K. S. Nisar

doi:10.1109/SSD.2016.7473657

Identification of ARX hammerstein models based on twin support vector machine regression

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

Abstract

In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ϵ-insensitive loss functions. One of them determines the ϵ1-insensitive down bound regressor while the other determines the ϵ1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.

Original language	English
Title of host publication	13th International Multi-Conference on Systems, Signals and Devices, SSD 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	571-582
Number of pages	12
ISBN (Electronic)	9781509012916
DOIs	https://doi.org/10.1109/SSD.2016.7473657
State	Published - 18 May 2016
Externally published	Yes

Publication series

Name	13th International Multi-Conference on Systems, Signals and Devices, SSD 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

Signal Processing
Control and Systems Engineering
Energy Engineering and Power Technology
Control and Optimization
Computer Networks and Communications
Instrumentation

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10.1109/SSD.2016.7473657

Cite this

Aldhaifallah, M., & Nisar, K. S. (2016). Identification of ARX hammerstein models based on twin support vector machine regression. In 13th International Multi-Conference on Systems, Signals and Devices, SSD 2016 (pp. 571-582). Article 7473657 (13th International Multi-Conference on Systems, Signals and Devices, SSD 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD.2016.7473657

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abstract = "In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ϵ-insensitive loss functions. One of them determines the ϵ1-insensitive down bound regressor while the other determines the ϵ1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.",

author = "Mujahed Aldhaifallah and Nisar, {K. S.}",

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year = "2016",

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doi = "10.1109/SSD.2016.7473657",

language = "English",

series = "13th International Multi-Conference on Systems, Signals and Devices, SSD 2016",

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Aldhaifallah, M & Nisar, KS 2016, Identification of ARX hammerstein models based on twin support vector machine regression. in 13th International Multi-Conference on Systems, Signals and Devices, SSD 2016., 7473657, 13th International Multi-Conference on Systems, Signals and Devices, SSD 2016, Institute of Electrical and Electronics Engineers Inc., pp. 571-582. https://doi.org/10.1109/SSD.2016.7473657

Identification of ARX hammerstein models based on twin support vector machine regression. / Aldhaifallah, Mujahed; Nisar, K. S.
13th International Multi-Conference on Systems, Signals and Devices, SSD 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 571-582 7473657 (13th International Multi-Conference on Systems, Signals and Devices, SSD 2016).

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

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N2 - In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ϵ-insensitive loss functions. One of them determines the ϵ1-insensitive down bound regressor while the other determines the ϵ1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.

AB - In this paper we develop a new algorithm to identify Auto-Regressive Exogenous (ARX) input Hammerstein Models based on Twin Support Vector Machine Regression (TSVR). The model is determined by minimizing two ϵ-insensitive loss functions. One of them determines the ϵ1-insensitive down bound regressor while the other determines the ϵ1-insensitive up bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation.

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Aldhaifallah M, Nisar KS. Identification of ARX hammerstein models based on twin support vector machine regression. In 13th International Multi-Conference on Systems, Signals and Devices, SSD 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 571-582. 7473657. (13th International Multi-Conference on Systems, Signals and Devices, SSD 2016). doi: 10.1109/SSD.2016.7473657

Identification of ARX hammerstein models based on twin support vector machine regression

Abstract

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Bibliographical note

ASJC Scopus subject areas

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Cite this