SYSTEM IDENTIFICATION BASED ON MULTI-KERNEL LEAST SQUARES SUPPORT VECTOR MACHINES (MULTI-KERNEL LS-SVM)

Mounira Tarhouni, Kaouther Laabidi, Moufida Lahmari-Ksouri, Salah Zidi

Abstract

This paper develops a new approach to identify nonlinear systems. A Multi-Kernel Least Squares Support Vector Machine (Multi-Kernel LS-SVM) is proposed. The basic LS-SVM idea is to map linear inseparable input data into a high dimensional linear separable feature space via a nonlinear mapping technique (kernel function) and to carry out linear classification or regression in feature space. The choice of kernel function is an important task which is related to the system nonlinearity degrees. The suggested approach combines several kernels in order to take advantage of their performances. Two examples are given to illustrate the effectiveness of the proposed method.

References

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Paper Citation


in Harvard Style

Tarhouni M., Laabidi K., Lahmari-Ksouri M. and Zidi S. (2010). SYSTEM IDENTIFICATION BASED ON MULTI-KERNEL LEAST SQUARES SUPPORT VECTOR MACHINES (MULTI-KERNEL LS-SVM) . In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010) ISBN 978-989-8425-32-4, pages 310-315. DOI: 10.5220/0003077703100315


in Bibtex Style

@conference{icnc10,
author={Mounira Tarhouni and Kaouther Laabidi and Moufida Lahmari-Ksouri and Salah Zidi},
title={SYSTEM IDENTIFICATION BASED ON MULTI-KERNEL LEAST SQUARES SUPPORT VECTOR MACHINES (MULTI-KERNEL LS-SVM)},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)},
year={2010},
pages={310-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003077703100315},
isbn={978-989-8425-32-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation - Volume 1: ICNC, (IJCCI 2010)
TI - SYSTEM IDENTIFICATION BASED ON MULTI-KERNEL LEAST SQUARES SUPPORT VECTOR MACHINES (MULTI-KERNEL LS-SVM)
SN - 978-989-8425-32-4
AU - Tarhouni M.
AU - Laabidi K.
AU - Lahmari-Ksouri M.
AU - Zidi S.
PY - 2010
SP - 310
EP - 315
DO - 10.5220/0003077703100315