scholarly journals Comparative Study of Distance Discriminant Analysis and Bp Neural Network for Identification of Rapeseed Cultivars Using Visible/Near Infrared Spectra

2011 ◽  
pp. 124-133 ◽  
Author(s):  
Qiang Zou ◽  
Hui Fang ◽  
Fei Liu ◽  
Wenwen Kong ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Discriminant Analysis ◽  
Comparative Study ◽  
Infrared Spectra ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Distance Discriminant Analysis

scholarly journals Non-destructive discrimination of plastic wastes by combining near-infrared spectra measurement and neural network analysis.

1999 ◽  
Vol 48 (5) ◽  
pp. 483-489 ◽  
Author(s):  
Takatoshi MATSUMOTO ◽  
Kazutoshi TANABE ◽  
Kazumitsu SAEKI ◽  
Toshio AMANO ◽  
Hiroyuki UESAKA
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Neural Network Analysis ◽  
Near Infrared Spectra ◽  
Plastic Wastes ◽  
Non Destructive

Artificial neural network and SIMCA classification in some wood discrimination based on near-infrared spectra

2017 ◽  
Vol 51 (4) ◽  
pp. 929-942 ◽  
Author(s):  
Silvana Nisgoski ◽  
André Anastácio de Oliveira ◽  
Graciela Inés Bolzon de Muñiz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Artificial Neural

scholarly journals Determination of Fiber Contents in Blended Textiles by NIR Combined with BP Neural Network

ISRN Textiles ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yaocheng Xie ◽  
Jie Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Back Propagation ◽  
Nir Spectroscopy ◽  
Fiber Content ◽  
Cotton Wool ◽  
Near Infrared Spectra ◽  
Hidden Neurons

Fiber contents in cotton/terylene and cotton/wool blended textiles were tested by near infrared (NIR) spectroscopy combined with back propagation (BP) neural network. Near infrared spectra of samples were obtained in the range of 4000 cm−1~10000 cm−1. Wavelet Transform (WT) was used for noise reduction and compression of spectra data. The correction models of cotton/terylene and cotton/wool contents based on BP neural network and reconstructed spectral signals were established. The number of hidden neurons, learning rate, momentum factor, and learning times was optimized, and decomposition scale of WT was discussed. Experimental results have shown that this approach by Fourier transformation NIR based on the BP neural network to predict the fiber content of textile can satisfy the requirement of quantitative analysis and is also suitable for other fiber content measurements of blended textiles.

scholarly journals Identification of mineral components from near-infrared spectra by a neural network.

1994 ◽  
Vol 43 (10) ◽  
pp. 765-769 ◽  
Author(s):  
Kazutoshi TANABE ◽  
Hiroyuki UESAKA ◽  
Tsuneshi INOUE ◽  
Hiroyuki TAKAHASHI ◽  
Soichiro TANAKA
Keyword(s):  
Neural Network ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Mineral Components

scholarly journals An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234557
Author(s):  
Masabho P. Milali ◽  
Samson S. Kiware ◽  
Nicodem J. Govella ◽  
Fredros Okumu ◽  
Naveen Bansal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Artificial Neural
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