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

International Journal of Applied Mathematics and Computer Science (2011)

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

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topTarek 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 -

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