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Proceedings Paper

Curve fitting for standard lamp of spectral irradiance based on RBFNN
Author(s): Binhua Chen; Caihong Dai; Zhifeng Wu; Lei Fu
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Paper Abstract

To reduce the uncertainty of dissemination, the models for standard lamp of spectral irradiance data are presented. We propose a divide-and-conquer RBF neural network approach in which the spectral irradiance is divided into two subsets, and each subset is modeled with a different network. The results show that the RBF neural network model produces well generalizations while the Planck-polynomial model produces poor ones. During the generalizations, the maximum relative deviation of the RBF neural network model and the Planck-polynomial model were 0.027% and 3.46%, respectively.

Paper Details

Date Published: 19 December 2013
PDF: 8 pages
Proc. SPIE 9046, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 904613 (19 December 2013); doi: 10.1117/12.2037491
Show Author Affiliations
Binhua Chen, Beijing Institute of Technology (China)
Caihong Dai, National Institute of Metrology (China)
Zhifeng Wu, National Institute of Metrology (China)
Lei Fu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 9046:
2013 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems
Hwa-Yaw Tam; Kexin Xu; Hai Xiao; Jigui Zhu, Editor(s)

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