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

• Autorzy: Tarek A. Mahmoud
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

least squares support vector machine; multi-resolution wavelet least squares support vector machine neural network; nonlinear system modeling and control; pH control

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2011
• Tom: 21
• Numer: 4
• Strony: 685-696

Daty

wydano

2011

otrzymano

2010-12-21

poprawiono

2011-05-04

poprawiono

2011-07-08

Twórcy

autor

Tarek A. Mahmoud

• Industrial Electronics and Control Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, El-Menoufiya, Egypt

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Identyfikatory

DOI

10.2478/v10006-011-0054-6