期刊
NEURAL COMPUTING & APPLICATIONS
卷 23, 期 2, 页码 283-292出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-012-0891-8
关键词
Surface reconstruction; Feedforward neural networks; Extreme learning machine; Polyharmonic extreme learning machine
资金
- National Natural Science Foundation of China [61101240]
- Zhejiang Provincial Natural Science Foundation of China [Y6110117]
- Science Foundation of Zhejiang Education Office [Y201122002]
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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