Spectroscopy and Spectral Analysis, Volume. 33, Issue 5, 1392(2013)
Research on the Application of Principal Component Analysis and Improved BP Neural Network to the Determination of Fe and Ti Contents in Geological Samples
Aiming at forecasting elemental contents in geological samples accurately, a principal component analysis and improved BP (PCA-BP) neural network theory is proposed in the present work. The samples from west Tianshan were measured through X-ray fluorescence measurement method, and the X-Ray fluorescence counts of each element such as Fe, Ti, V, Pb, Zn, etc. were input to the PCA-BP neural network as input variables to forecast Fe and Ti contents in uncertified geological samples quantitatively. The results show that the PCA-BP neural network can give an ideal result, and the relative error between the forecast data and chemical analysis data is less than 3%. This method provides a new and effective approach to forecasting elemental contents in geological samples.
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XU Li-peng, GE Liang-quan, GU Yi, LIU Min, ZHANG Qing-xian, LI Fei, LUO Bin. Research on the Application of Principal Component Analysis and Improved BP Neural Network to the Determination of Fe and Ti Contents in Geological Samples[J]. Spectroscopy and Spectral Analysis, 2013, 33(5): 1392
Received: Jul. 23, 2012
Accepted: --
Published Online: May. 21, 2013
The Author Email: Li-peng XU (xulipeng-cdut@163.com)