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- Title
Marine Mineral Classification Based on Single-Output Chebyshev-Polynomial Neural Network.
- Authors
JIN Long; CHEN Xiufang; CHEN Liangming; FU Jinshan
- Abstract
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.
- Subjects
MARINE mineral resources; CHEBYSHEV polynomials; CLASSIFICATION; ARTIFICIAL neural networks; MATHEMATICAL models
- Publication
Journal of South China University of Technology (Natural Science Edition), 2020, Vol 48, Issue 12, p135
- ISSN
1000-565X
- Publication type
Article
- DOI
10.12141/j.issn.1000-565X.200389