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.
