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Authors: Mounira Tarhouni 1 ; Kaouther Laabidi 1 ; Moufida Lahmari-Ksouri 1 and Salah Zidi 2

Affiliations: 1 Unit of Research Analysis and control of systems (ACS and ENIT), Tunisia ; 2 LAGIS (USTL and Lille), France

Keyword(s): Nonlinear system identification, Least Squares Support Vector Machines (LS-SVM), Multi-kernel function, Multi model, Weighted function.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

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.

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Paper citation in several formats:
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 (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 310-315. DOI: 10.5220/0003077703100315

@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 (IJCCI 2010) - ICNC},
year={2010},
pages={310-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003077703100315},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
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
PB - SciTePress

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