Least squares support kernel machines (LS-SKM) for identification Online publication date: Wed, 17-Dec-2014
by Mounira Tarhouni; Salah Zidi; Kaouther Laabidi; M. Ksouri-Lahmari
International Journal of Modelling, Identification and Control (IJMIC), Vol. 17, No. 1, 2012
Abstract: This paper presents a novel approach for non-linear systems identification called 'least squares support kernel machines (LS-SKM)'. Instead of using a least squares support vector machines (LS-SVM) with a single kernel function, the proposed approach combines several kernels in order to take advantage of their performances and also reflects the fact that practical learning problems often involve multiple, heterogeneous data sources. The idea is to divide the regressor vector in several regressor vectors, and, for each vector a kernel function is used. The choice of kernel function and the corresponding parameters is an important task which is related to the non-linear system degrees. A constrained particle swarm optimisation (CPSO) is used to give solution for the determination of optimised kernel parameters. Two examples are presented for qualitative comparison with the classical LS-SVM. The results reveal the accuracy and the robustness of the obtained model based on our proposed hybrid method.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook