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Prediction of Hydrogen Content in Coal using Back Propagation Neural Network
Author(s) -
Ju L. C.,
Tadé M. O.,
Zhu J. N.,
Yao H. M.
Publication year - 2005
Publication title -
developments in chemical engineering and mineral processing
Language(s) - English
Resource type - Journals
eISSN - 1932-2143
pISSN - 0969-1855
DOI - 10.1002/apj.5500130116
Subject(s) - artificial neural network , backpropagation , coal , convergence (economics) , sample (material) , computer science , artificial intelligence , data mining , engineering , chemistry , waste management , chromatography , economics , economic growth
This paper introduces the concept of sample study risk in neural network (NN), and studies the prediction of hydrogen content in coal using Back Propagation Neural Networks (BP NN). Targeting the problem of training convergence quality impaired by the interfering information of some samples in BP NN, the validity of the concept of sample study in NN, and the feasibility of analyzing chemical elements in coal using NN are discussed.