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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2013

01.08.2013 | Original Article

Surface reconstruction based on extreme learning machine

verfasst von: Zheng Hua Zhou, Jian Wei Zhao, Fei Long Cao

Erschienen in: Neural Computing and Applications | Ausgabe 2/2013

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Abstract

In this paper, extreme learning machine (ELM) is used to reconstruct a surface with a high speed. It is shown that an improved ELM, called polyharmonic extreme learning machine (P-ELM), is proposed to reconstruct a smoother surface with a high accuracy and robust stability. The proposed P-ELM improves ELM in the sense of adding a polynomial in the single-hidden-layer feedforward networks to approximate the unknown function of the surface. The proposed P-ELM can not only retain the advantages of ELM with an extremely high learning speed and a good generalization performance but also reflect the intrinsic properties of the reconstructed surface. The detailed comparisons of the P-ELM, RBF algorithm, and ELM are carried out in the simulation to show the good performances and the effectiveness of the proposed algorithm.
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Metadaten
Titel
Surface reconstruction based on extreme learning machine
verfasst von
Zheng Hua Zhou
Jian Wei Zhao
Fei Long Cao
Publikationsdatum
01.08.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0891-8

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