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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The research was supported by the National Natural Science Foundation of China(No. 61101240), the Zhejiang Provincial Natural Science Foundation of China (No. Y6110117), and the Science Foundation of Zhejiang Education Office (No. Y201122002).
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Zhou, Z.H., Zhao, J.W. & Cao, F.L. Surface reconstruction based on extreme learning machine. Neural Comput & Applic 23, 283–292 (2013). https://doi.org/10.1007/s00521-012-0891-8
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DOI: https://doi.org/10.1007/s00521-012-0891-8