Predictive Model for Chinese Excavated Glass Based on Least Squares Method and BP-Neural Network

DOI

10.2991/978-94-6463-198-2_105How to use a DOI?

Keywords

Antient Chinese glass; partial least squares regression; predictive model; PB neural network models

Abstract

The ancient glass excavated in China proves that at least in the Warring States period more than 2,000 years ago, it was already possible to manufacture glass products with exquisite patterns. The antique Chinese lead-barium glass and potassium glass represent China's indigenous glass technology system, and their study has significantly contributed to the history of Chinese science and technology. To this end, this paper provides a physical examination of a group of more representative ancient Chinese lead-barium glass and potassium glass. Old glass is susceptible to weathering by composition and environment. Environmental factors mainly refer to the temperature, humidity, and time when storing glass. Therefore, the weathering products on the glass surface are determined by the glass composition, temperature, humidity, time, and atmosphere at the weathering time. The ratio of the chemical composition of the weathered glass will change and thus affect the judgment of the glass type. Therefore, this study developed partial least squares regression and PB neural network models to predict the chemical composition of lead-barium glass and potassium glass from a group of Chinese excavations.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junyi Song
PY  - 2023
DA  - 2023/08/10
TI  - Predictive Model for Chinese Excavated Glass Based on Least Squares Method and BP-Neural Network
BT  - Proceedings of the 2nd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2023)
PB  - Atlantis Press
SP  - 1018
EP  - 1024
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-198-2_105
DO  - 10.2991/978-94-6463-198-2_105
ID  - Song2023
ER  -