DocumentCode :
480219
Title :
The Fluorescence Spectroscopy Recognition of the Mineral Oil Based on the Multiresolution Orthogonal Multiwavelet Neural Network
Author :
Jiangtao, Lv ; Yutian, Wang ; Zhao, Pan
Author_Institution :
Meas. Technol. & Instrum. Key Lab. of Hebei Province, Yanshan Univ., Qin Huangdao
Volume :
4
fYear :
2008
fDate :
12-14 Dec. 2008
Firstpage :
785
Lastpage :
787
Abstract :
The singular value eigenvectors of the different kinds of the mineral oil stylebooks are obtained by parameterizing the three-dimensional fluorescence spectroscopy. They are complicated and not easy to be recognized by the simple formula. The multiwavelet neural network is introduced to realize the identification of the different kinds of the mineral oil. It was layered. It had the feature of the part study. The prompting function of the network is constructed by the multiscale function and multiwavelet function. The experiment indicates that the network has all the virtue of the wavelet neural network (WNN). It also has the much better approach property than the WNN. It can effectively recognize the fine distinction between the different spectrums and realize the identification of the oil by much fewer train times than the WNN.
Keywords :
eigenvalues and eigenfunctions; fluorescence spectroscopy; mineral processing industry; neural nets; oils; petroleum industry; singular value decomposition; wavelet transforms; fluorescence spectroscopy recognition; mineral oil stylebooks; multiresolution orthogonal multiwavelet neural network; multiscale function; singular value eigenvectors; Biological neural networks; Computer science; Convergence; Fluorescence; Minerals; Neural networks; Petroleum; Software engineering; Software measurement; Spectroscopy; fluorescence spectroscopy; mineral oil; multiwavelet neural network; spectral recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
Type :
conf
DOI :
10.1109/CSSE.2008.999
Filename :
4722736
Link To Document :
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