Identification of auto-regressive exogenous models based on twin support vector machine regression

Mujahed Aldhaifallah; K. S. Nisar

Identification of auto-regressive exogenous models based on twin support vector machine regression

Mujahed Aldhaifallah^*, K. S. Nisar

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. 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 ε₂-insensitive up-bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation and experimental data.

Original language	English
Article number	406
Pages (from-to)	3049-3054
Number of pages	6
Journal	Life Science Journal
Volume	10
Issue number	4
State	Published - 2013

Keywords

Auto-Regressive Exogenous Models
Identification
Twin Support Vector Machines

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology

Cite this

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title = "Identification of auto-regressive exogenous models based on twin support vector machine regression",

abstract = "In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. 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 ε2-insensitive up-bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation and experimental data.",

keywords = "Auto-Regressive Exogenous Models, Identification, Twin Support Vector Machines",

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

year = "2013",

language = "English",

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pages = "3049--3054",

journal = "Life Science Journal",

issn = "1097-8135",

publisher = "Zhengzhou University",

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T1 - Identification of auto-regressive exogenous models based on twin support vector machine regression

AU - Aldhaifallah, Mujahed

AU - Nisar, K. S.

PY - 2013

Y1 - 2013

N2 - In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. 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 ε2-insensitive up-bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation and experimental data.

AB - In this paper a new algorithm to identify Auto-Regressive Exogenous Models (ARX) based on Twin Support Vector Machine Regression (TSVR) has been developed. 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 ε2-insensitive up-bound regressor. The algorithm is compared to Support Vector Machine (SVM) and Least Square Support Vector Machine (LSSVM) based algorithms using simulation and experimental data.

KW - Auto-Regressive Exogenous Models

KW - Identification

KW - Twin Support Vector Machines

UR - https://www.scopus.com/pages/publications/84891524908

M3 - Article

AN - SCOPUS:84891524908

SN - 1097-8135

VL - 10

SP - 3049

EP - 3054

JO - Life Science Journal

JF - Life Science Journal

IS - 4

M1 - 406

ER -

Identification of auto-regressive exogenous models based on twin support vector machine regression

Abstract

Keywords

ASJC Scopus subject areas

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Cite this