A study of ancient glass based on a modeling perspective
DOI:
https://doi.org/10.54097/hset.v40i.6603Keywords:
Chi-square test, Decision tree classification, BP neural network prediction.Abstract
As a valuable physical evidence of the cultural exchange between China and the West in ancient times, ancient glass is of great research significance. However, ancient glass is highly susceptible to weathering by the burial environment, and previous studies have only applied statistical methods for simple classification and generalization. In order to further study the composition analysis and prediction of ancient glass, glass subclass classification and identification, this paper constructs a data prediction model based on the theory of chi-square test, decision tree classification, and BP neural network prediction, so that the research on ancient glass can broaden the current research progress from a new perspective.
Downloads
References
LI H. Origin of the art of glass-making in China [J]. Glass Technology-European Journal of Glass Science and Technology Part A, 2018, 59 (1): 20 - 8.
CHOPINET M H, VERITA M, FALCONE R, et al. Soda-lime-silica glass containers: chemical durability and weathering products; proceedings of the 9th Conference of the European-Society-of-Glass-Science-and-Technology/Annual Meeting of the International-Commission-on-Glass, Trencin, SLOVAKIA, F Jun 22-26, 2008 [C]. 2008.
WU J, QI H E, XINGWEI D U, et al. Prediction of crab Yield by BP neural network based on meteorological factors of producing areas in Jiangsu Province [J]. 2020.
BERGH D. Chi-Squared Test of Fit and Sample Size-A Comparison between a Random Sample Approach and a Chi-Square Value Adjustment Method [J]. Journal of applied measurement, 2015, 16 (2): 204 - 17.
JING H, ZHAO X A. The application of improved grey multi-hierarchical comprehensive evaluation method to the growth ability evaluation of technological small and medium enterprises; proceedings of the 26th Chinese Control Conference, Zhangjiajie, PEOPLES R CHINA, F Jul 26-31, 2007 [C]. 2007.
QUINLAN J R. Learning decision tree classifiers [J]. ACM Computing Surveys (CSUR), 1996, 28 (1): 71 - 2.
LIU L H, CHEN J, XU L X. Realization and application research of BP neural network based on MATLAB; proceedings of the International Seminar on Future BioMedical Information Engineering, Wuhan, PEOPLES R CHINA, F Dec 18, 2008 [C]. 2008.
JAIN A K. Data clustering: 50 years beyond K-means [J]. Pattern Recognition Letters, 2010, 31(8): 651 - 66.
KRISHNA K, MURTY M N. Genetic K-means algorithm [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999, 29 (3): 433 - 9.
ORCHARD J. Multimodal image registration using floating regressors in the joint intensity scatter plot [J]. Medical image analysis, 2008, 12 (4): 385 - 96.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
