| 摘 要: 为研究不同类别的古代玻璃的内在化学成分与自身风化程度的关系,建立了古代玻璃类别预测及风化程度测定模型。首先,利用GA-BP神经网络,以高收敛速度及97.8%的高拟合优度准确预测样品玻璃的真实类别,同时克服传统BP 神经网络容易陷入局部最小值的问题。其次,根据样品类别的不同,分别对其采用主成分分析降维和熵权法,得到样品玻璃的未风化程度指数,其中高钾玻璃的15号样品和铅钡玻璃的37号样品受损较严重。实验结果显示:该模型可以很好地测算古代玻璃的相关参数,可被广泛应用于考古行业的玻璃文物参数分析与测算工作。 |
| 关键词: 古代玻璃分类;GA-BP神经网络;主成分分析降维;熵权法 |
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中图分类号: TP319
文献标识码: A
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| Research on Ancient Glass Composition Prediction and Weathering Degree Measurement Model Based on GA-BP |
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CAO Yuxuan, SUI Guorong
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(School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)
2035060509@st.usst.edu.cn; suigr@usst.edu.cn
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| Abstract: In order to study the relationship between the intrinsic chemical composition of different types of ancient glass and their own degree of weathering, this paper proposes to establish a model for predicting ancient glass composition and measuring their weathering degree. Firstly, GA-BP (Genetic Algorithm-Backpropagation) uses neural network to accurately predict the true composition of sample glass with high rate of convergence and high goodness of fit of 97.8% , while overcoming the problem that traditional BP neural network is prone to fall into local minimum. Secondly, according to the different types of samples, dimensionality reduction by principal component analysis and entropy weight method are used to obtain the unweathered degree index of the sample glass. Among them, sample 15 of high potassium glass and sample 37 of lead barium glass are severely damaged. The experimental results show that this model can effectively calculate the relevant parameters of ancient glass and can be widely applied in the analysis and calculation of glass cultural relics parameters in the archaeological field. |
| Keywords: classification of ancient glass; GA-BP neural network; dimensionality reduction by principal component; entropy weight method |
