LS-SVM approximate solution for affine nonlinear systems with partially unknown functions

By using the Least Squares Support Vector Machines (LS-SVMs), we develop a numerical approach to find an approximate solution for affine nonlinear systems with partially unknown functions. This approach can obtain continuous and differential approximate solutions of the nonlinear differential equati...

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Main Authors: Zhang, G., Wang, S., Wang, Y., Liu, Wan-Quan
Format: Journal Article
Published: American Institute of Mathematical Sciences 2014
Online Access:http://hdl.handle.net/20.500.11937/49945
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author Zhang, G.
Wang, S.
Wang, Y.
Liu, Wan-Quan
author_facet Zhang, G.
Wang, S.
Wang, Y.
Liu, Wan-Quan
author_sort Zhang, G.
building Curtin Institutional Repository
collection Online Access
description By using the Least Squares Support Vector Machines (LS-SVMs), we develop a numerical approach to find an approximate solution for affine nonlinear systems with partially unknown functions. This approach can obtain continuous and differential approximate solutions of the nonlinear differential equations, and can also identify the unknown nonlinear part through a set of measured data points. Technically, we first map the known part of the affine nonlinear systems into high dimensional feature spaces and derive the form of approximate solution. Then the original problem is formulated as an approximation problem via kernel trick with LS-SVMs. Furthermore, the approximation of the known part can be expressed via some linear equations with coefficient matrices as coupling square matrices, and the unknown part can be identified by its relationship to the known part and the approximate solution of affine nonlinear systems. Finally, several examples for different systems are presented to illustrate the validity of the proposed approach.
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institution Curtin University Malaysia
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last_indexed 2025-11-14T09:42:41Z
publishDate 2014
publisher American Institute of Mathematical Sciences
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spelling curtin-20.500.11937-499452017-09-13T15:50:49Z LS-SVM approximate solution for affine nonlinear systems with partially unknown functions Zhang, G. Wang, S. Wang, Y. Liu, Wan-Quan By using the Least Squares Support Vector Machines (LS-SVMs), we develop a numerical approach to find an approximate solution for affine nonlinear systems with partially unknown functions. This approach can obtain continuous and differential approximate solutions of the nonlinear differential equations, and can also identify the unknown nonlinear part through a set of measured data points. Technically, we first map the known part of the affine nonlinear systems into high dimensional feature spaces and derive the form of approximate solution. Then the original problem is formulated as an approximation problem via kernel trick with LS-SVMs. Furthermore, the approximation of the known part can be expressed via some linear equations with coefficient matrices as coupling square matrices, and the unknown part can be identified by its relationship to the known part and the approximate solution of affine nonlinear systems. Finally, several examples for different systems are presented to illustrate the validity of the proposed approach. 2014 Journal Article http://hdl.handle.net/20.500.11937/49945 10.3934/jimo.2014.10.621 American Institute of Mathematical Sciences unknown
spellingShingle Zhang, G.
Wang, S.
Wang, Y.
Liu, Wan-Quan
LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title_full LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title_fullStr LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title_full_unstemmed LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title_short LS-SVM approximate solution for affine nonlinear systems with partially unknown functions
title_sort ls-svm approximate solution for affine nonlinear systems with partially unknown functions
url http://hdl.handle.net/20.500.11937/49945