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Neural Computing and Applications

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01-08-2013 | Original Article

Surface reconstruction based on extreme learning machine

Authors: Zheng Hua Zhou, Jian Wei Zhao, Fei Long Cao

Published in: Neural Computing and Applications | Issue 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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Title: Surface reconstruction based on extreme learning machine
Authors: Zheng Hua Zhou
Jian Wei Zhao
Fei Long Cao
Publication date: 01-08-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0891-8

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