Support Vector Machine Identification of Subspace Hammerstein Models - Volume 7, Number 1 (Feb. 2015) - IJCTE
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Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2015 Vol.7(1): 9-15 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2015.V7.922

Support Vector Machine Identification of Subspace Hammerstein Models

Mujahed Al Dhaifallah and K. S. Nisar
Abstract—In this work a new method for identifying subspace Hammerstein systems based on Support vector machine regression is presented. It has been developed by modifying a least-square support vector machine based approach presented earlier. The new algorithm exploits the properties of generic SVM which LS-SVM based algorithm lacks. These properties are robustness in the presence of outliers and sparseness of solution. The proposed algorithm is reduced to include the least number of quadratic programming problems needed to estimate the system matrices and nonlinearity which in turn will reduce the computation complexity of the algorithm.

Index Terms—Hammerstein models, subspace identification, support vector machines.

Mujahed Al Dhaifallah is with the Department of Systems Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Kingdom of Saudi Arabia (e-mail: mujahed@kfupm.edu.sa).
K. S. Nisar is with the Department of Mathematics, Salman bin Abdulaziz University, Wadi Al Dawaser, Kingdom of Saudi Arabia (e-mail: ksnisar1@gmail.com).

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Cite:Mujahed Al Dhaifallah and K. S. Nisar, "Support Vector Machine Identification of Subspace Hammerstein Models," International Journal of Computer Theory and Engineering vol. 7, no. 1, pp. 9-15, 2015.

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