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Study on the High-Speed Analysis of Coal Qualities by FT-NIR Method Based on Improved Successive Projections Algorithm

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Abstract:

To solve the problems of high dimension, multicollinearity, easily over-fitting, this paper further studies characteristic wavelength selection method, and proposes the improved successive projections algorithm based on mean impact value algorithm (SPA-MIV). The results show that the proposed algorithm reduces data dimensions and improves data quality effectively. After being processed by improved successive projections algorithm, the determination coefficient of of testing set of moisture, ash and volatile PLS calibration models are increased to 0.9318, 0.9127, 0.9389, and the of testing set determination coefficient of moisture, ash and volatile BP neural network calibration models are 0.9645,0.9432,0.9536.

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Periodical:

Advanced Materials Research (Volume 1094)

Pages:

174-180

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1094.174

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Online since:

March 2015

Authors:

Zhi Bin Xu*, Xue Bing Si, Cui Li, Fan Chen

Keywords:

BP Neural Network, Ft-NIR, Successive Projections Algorithm (SPA), Wavelength Selection

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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