Journal of South China University of Technology(Natural Science) >
Marine Mineral Classification Based on Single-Output Chebyshev-Polynomial Neural Network
Received date: 2020-07-06
Revised date: 2020-07-07
Online published: 2020-07-10
Supported by
Supported by the National Key Research and Development Program of China ( 2017YFE0118900) ，the National
Natural Science Foundation of China ( 61703189，11561029) ，the Key Project of the Natural Science Foundation of Gansu Province ( 18JR3RA264) ，the Team Project of Natural Science Foundation of Qinghai Province ( 2020-ZJ-903) ，the Opening Fund of
Acoustics Science and Technology Laboratory ( SSKF2018005) and the Fundamental Research Funds for the Central Universities
( lzujbky-2019-89)
Aiming at the classification of marine minerals，an improved single-output Chebyshev-polynomial neural network with general solution ( SOCPNN-G) was proposed． This model uses the general solution of pseudo-inverse to find the parameters and expand the solution space，and it can obtain weights with better generalization performances． In addition，in this model，the subset method was used to determine the initial number of neurons and obtain the optimal number of the cross validation． Finally，the modified SOCPNN-G was tested in the marine mineral data set． The experimental results show that the training accuracy and test accuracy of the model can reach 90. 96% and 83. 33% ，respectively，and the requirements for computing performance are low． These advantages indicate that this model has excellent application prospects in marine minerals．
JIN Long, CHEN Xiufang, CHEN Liangming, et al . Marine Mineral Classification Based on Single-Output Chebyshev-Polynomial Neural Network[J]. Journal of South China University of Technology(Natural Science), 2020 , 48(12) : 135 -143 . DOI: 10.12141/j.issn.1000-565X.200389
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