Adaptive control scheme based on the least squares support vector machine network

Tarek A. Mahmoud

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 4, page 685-696
  • ISSN: 1641-876X

Abstract

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Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.

How to cite

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Tarek A. Mahmoud. "Adaptive control scheme based on the least squares support vector machine network." International Journal of Applied Mathematics and Computer Science 21.4 (2011): 685-696. <http://eudml.org/doc/208080>.

@article{TarekA2011,
abstract = {Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.},
author = {Tarek A. Mahmoud},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {least squares support vector machine; multi-resolution wavelet least squares support vector machine neural network; nonlinear system modeling and control; pH control},
language = {eng},
number = {4},
pages = {685-696},
title = {Adaptive control scheme based on the least squares support vector machine network},
url = {http://eudml.org/doc/208080},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Tarek A. Mahmoud
TI - Adaptive control scheme based on the least squares support vector machine network
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 4
SP - 685
EP - 696
AB - Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
LA - eng
KW - least squares support vector machine; multi-resolution wavelet least squares support vector machine neural network; nonlinear system modeling and control; pH control
UR - http://eudml.org/doc/208080
ER -

References

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