Learning spectrum's selection in OLAM network for analysis cement samples

Ning Huang; Peng Wang; Dai Quan Tang; Ren Lan Hu

Learning spectrum's selection in OLAM network for analysis cement samples

Ning Huang, Peng Wang, Dai Quan Tang, Ren Lan Hu

Research output: Contribution to journal › Article › peer-review

Abstract

It uses OLAM artificial neural network to analyze the samples of cement raw material Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results.

Original language	English
Pages (from-to)	93-95+70
Journal	Hedianzixue Yu Tance Jishu/Nuclear Electronics and Detection Technology
Volume	30
Issue number	1
State	Published - Jan 2010
Externally published	Yes

Keywords

Learning Spectrum Selection
OLAM
Raw material of Cement
XRF

Cite this

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title = "Learning spectrum's selection in OLAM network for analysis cement samples",

abstract = "It uses OLAM artificial neural network to analyze the samples of cement raw material Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results.",

keywords = "Learning Spectrum Selection, OLAM, Raw material of Cement, XRF",

author = "Ning Huang and Peng Wang and Tang, {Dai Quan} and Hu, {Ren Lan}",

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AU - Wang, Peng

AU - Tang, Dai Quan

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N2 - It uses OLAM artificial neural network to analyze the samples of cement raw material Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results.

AB - It uses OLAM artificial neural network to analyze the samples of cement raw material Two kinds of spectrums are used for network learning: pure-element spectrum and mix-element spectrum. The output of pure-element method can be used to construct a simulate spectrum, which can be compared with the original spectrum and judge the shift of spectrum; the mix-element method can store more message and correct the matrix effect, but the multicollinearity among spectrums can cause some side effect to the results.

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KW - Raw material of Cement

KW - XRF

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JF - Hedianzixue Yu Tance Jishu/Nuclear Electronics and Detection Technology

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ER -

Learning spectrum's selection in OLAM network for analysis cement samples

Abstract

Keywords

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