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Authors: Abdesselem Dakhli 1 ; Maher Jbeli 1 and Chokri Ben Amar 2

Affiliations: 1 Ha’il University, Kingdom of Saudi Arabia ; 2 Research Groups on Intelligent Machines (REGIM), ENIS, University of Sfax, Tunisia

Keyword(s): Wavelet Neural Networks, Least Trimmed Square, Multi Library Wavelet Function, Beta Wavelets.

Abstract: Wavelet neural networks have recently aroused great interest, because of their advantages compared to networks with radial basic functions because they are universal approximators. In this paper, we propose a robust wavelet neural network based on the Least Trimmed Square (LTS) method and Multi Library Wavelet Function (MLWF). We use a novel Beta wavelet neural network BWNN. A constructive neural network learning algorithm is used to add and train these additional neurons. The general goal of this algorithm is to minimize the number of neurons in the network during the learning phase. This phase is empowered by the use of Multi Library Wavelet Function (MLWF). The Least Trimmed Square (LTS) method is applied for selecting the wavelet candidates from the MLWF to construct the BWNN. A numerical experiment is given to validate the application of this wavelet neural network in multivariable functional approximation. The experimental results show that the proposed approach is very effecti ve and accurate. (More)

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Paper citation in several formats:
Dakhli, A.; Jbeli, M. and Ben Amar, C. (2020). Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 468-477. DOI: 10.5220/0009802604680477

@conference{iceis20,
author={Abdesselem Dakhli. and Maher Jbeli. and Chokri {Ben Amar}.},
title={Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2020},
pages={468-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009802604680477},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Functions Approximation using Multi Library Wavelets and Least Trimmed Square (LTS) Method
SN - 978-989-758-423-7
IS - 2184-4992
AU - Dakhli, A.
AU - Jbeli, M.
AU - Ben Amar, C.
PY - 2020
SP - 468
EP - 477
DO - 10.5220/0009802604680477
PB - SciTePress

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