Study on Composition Analysis and Identification of Ancient Glass Products
DOI:
https://doi.org/10.54097/hset.v29i.4839Keywords:
Entropy weight-TOPSIS method, Random forest, K-means, Grey Relational Analysis, Spearman's rank correlation coefficient.Abstract
In this paper, an entropy-TOPSIS model is established to analyze and identify the composition of ancient glass products, which solves the relationship between the type, decoration, colour of glass and the surface weathering of glass cultural relics; The multivariate linear regression model is established, which solves the problem of statistical law of weathering chemical composition content on the surface of cultural relics samples. A random forest classification model based on an ant colony algorithm is established to solve the problem of determining the importance of chemical composition, and a K-means clustering model is established to solve the problem of sub-class division within the group; A grey correlation analysis model was established to solve the problem of the correlation between the chemical composition of the same type of glass.
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Fu Weizhong, Chu Liuping. Research on the Evaluation of Manufacturing High Quality Development from the Perspective of Yangtze River Delta Integration -- TOPSIS Evaluation Model based on the improved CRITIC- entropy weight method combined weight [J]. Industrial technology and economics, 2020, 39(09): 145-152.
L Syafaah, D Arisnanda, M Irfan, R H Hendaryati. Correlation of risk factors for coronary heart disease in type-2 diabetes mellitus using the chi-square method [J]. IOP Conference Series Materials Science and Engineering, 2020, 821(1).
li xingqi, gao xiaohong. Dimensionless method of effectiveness evaluation [J]. Journal of statistics and decision, 2021 ((15): 24 to 28, DOI: 10.13546/j.carol carroll nki tjyjc. 2021.15.005.
Shen Ying. T test and F Application of inspection in Trend Analysis of chemical test quality control data [J]. Chinese journal of inspection and quarantine, 2019, 29(02): 105-107.
he xiuli. Multivariate Linear model and Ridge regression analysis [D]. Huazhong University of Science and Technology, 2005.
Yan Chun, Li Yaqi, Sun Haitang. Research on Auto Insurance Fraud Identification based on Ant Colony Optimization Random Forest Model [J]. Insurance research, 2017(6): 114-127. The DOI: 10.13497/j.carol carroll nki is. 2017.06.011.
Deng Yurui, xu-dong cheng, Tang Fang, zhou yong. Risk control of Rice mildew in storage based on multiple linear regression analysis and random forest algorithm [J]. Journal of University of Science and Technology of China, 202, 52(01): 47-54+72+55.
Tian Bing, Huang Shan, Sun Ye, Yan Yujie, Jin Kangkang. Rockburst Prediction based on SOM neural network clustering and gray TOPSIS evaluation method [J]. China mining, 2021, 30(01): 188-192.
guo wenjuan. K-means clustering algorithm based on Optimized initial clustering Center [J]. Wind technology, 2022(4): 63-65. The DOI: 10.19392/j.carol carroll nki. 1671-7341.202204021.
Wu Huijie. Study on Application and Optimization Method of random machine forest Algorithm [D]. Jiangnan great learning, 2021. DOI: 10.27169/, dc nki. Gwqgu. 2021.001718.
Zhang Weifeng. Statistical analysis of Spearman's simple correlation coefficient and Gini Gamma correlation coefficient [D]. wide East university of technology, 2020. DOI: 10.27029/, dc nki. Ggdgu. 2020.001333.
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