A LS-SVM modeling approach for nonlinear distributed parameter processes

Chenkun Qi; Han-Xiong Li

doi:10.1109/WCICA.2008.4592985

A LS-SVM modeling approach for nonlinear distributed parameter processes

Chenkun Qi
, Han-Xiong Li

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

14 Citations (Scopus)

Abstract

The distributed parameter system modeling from the input and output data is investigated. The spatio-temporal output of the system is measured at a finite number of spatial locations, while the input is assumed to be a finite-dimensional temporal variable. Firstly, Karhunen-Loève (KL) decomposition is used for the time/space separation and the dimension reduction. Subsequently the spatio-temporal output is expanded in terms of a low dimensional Karhunen-Loève spatial basis functions. Finally its temporal dynamic model is learned from the temporal coefficients by using least squares support vector machines (LSSVM). The simulations are presented to show the effectiveness of this spatio-temporal modeling method. © 2008 IEEE.

Original language	English
Title of host publication	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Pages	569-574
DOIs	https://doi.org/10.1109/WCICA.2008.4592985
Publication status	Published - 2008
Event	7th World Congress on Intelligent Control and Automation, WCICA'08 - Chongqing, China Duration: 25 Jun 2008 → 27 Jun 2008 https://ieeexplore.ieee.org/xpl/conhome/4577718/proceeding

Conference

Conference	7th World Congress on Intelligent Control and Automation, WCICA'08
Place	China
City	Chongqing
Period	25/06/08 → 27/06/08
Internet address	https://ieeexplore.ieee.org/xpl/conhome/4577718/proceeding

Research Keywords

Distributed parameter system
Karhunen-Loève decomposition
Least squares support vector machines
Spatio-temporal modeling

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10.1109/WCICA.2008.4592985

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title = "A LS-SVM modeling approach for nonlinear distributed parameter processes",

abstract = "The distributed parameter system modeling from the input and output data is investigated. The spatio-temporal output of the system is measured at a finite number of spatial locations, while the input is assumed to be a finite-dimensional temporal variable. Firstly, Karhunen-Lo{\`e}ve (KL) decomposition is used for the time/space separation and the dimension reduction. Subsequently the spatio-temporal output is expanded in terms of a low dimensional Karhunen-Lo{\`e}ve spatial basis functions. Finally its temporal dynamic model is learned from the temporal coefficients by using least squares support vector machines (LSSVM). The simulations are presented to show the effectiveness of this spatio-temporal modeling method. {\textcopyright} 2008 IEEE.",

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AU - Li, Han-Xiong

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AB - The distributed parameter system modeling from the input and output data is investigated. The spatio-temporal output of the system is measured at a finite number of spatial locations, while the input is assumed to be a finite-dimensional temporal variable. Firstly, Karhunen-Loève (KL) decomposition is used for the time/space separation and the dimension reduction. Subsequently the spatio-temporal output is expanded in terms of a low dimensional Karhunen-Loève spatial basis functions. Finally its temporal dynamic model is learned from the temporal coefficients by using least squares support vector machines (LSSVM). The simulations are presented to show the effectiveness of this spatio-temporal modeling method. © 2008 IEEE.

KW - Distributed parameter system

KW - Karhunen-Loève decomposition

KW - Least squares support vector machines

KW - Spatio-temporal modeling

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DO - 10.1109/WCICA.2008.4592985

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424421145

SP - 569

EP - 574

BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

T2 - 7th World Congress on Intelligent Control and Automation, WCICA'08

Y2 - 25 June 2008 through 27 June 2008

ER -

A LS-SVM modeling approach for nonlinear distributed parameter processes

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Research Keywords

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