Surface reconstruction based on extreme learning machine
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 improv...
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Published in | Neural computing & applications Vol. 23; no. 2; pp. 283 - 292 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.08.2013
Springer |
Subjects | |
Online Access | Get full text |
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Summary: | 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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ISSN: | 0941-0643 1433-3058 |
DOI: | 10.1007/s00521-012-0891-8 |