NDLI: Fluorescence optical fiber measurement system based on an advanced Neural Network (NN) ensemble algorithm

Content Provider	IEEE Xplore Digital Library
Author	Wang Yutian Wang Yingchao Yang Shiying
Copyright Year	2011
Description	Author affiliation: Hebei Key Laboratory of Measurement Technology and Instrumentation, Yanshan University, Qinhuangdao, China (Wang Yutian; Wang Yingchao; Yang Shiying)
Abstract	Noisy Spread based Ensemble Neural Networks is a new neural network ensemble algorithm with high integration, anti-interference and high precision. In this paper, it's applied to fluorescence spectra of the oil in water (classification and identification). The method uses optical spectroscopy to measure excitation fluorescence spectroscopy in the pool of crude oil and mineral oil in the range 230 ∼ 400nm, belong to UV spectral region: spectral interval of 5nm, recording fluorescence spectroscopy data, then the integration neural networks were established by the enhanced T individual neural networks. Vote method for the classification of fluorescence spectra characteristics and the average method for nonlinear regression. Experiments show that the neural network integrated for fluorescence spectral measurement system of oil in water identification and processing higher accuracy.
Starting Page	415
Ending Page	418
File Size	391248
Page Count	4
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5778096
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Accuracy Oil Noise Signal processing algorithms Artificial neural networks Fluorescence Nonlinear Regression Pollution measurement Ensemble Neural Networks Fluorescence Spectrum Spectral Anylisis
Content Type	Text
Resource Type	Article

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