论文标题
人工神经网络用于预测含铅眼镜的粘度
Artificial neural networks for predicting the viscosity of lead-containing glasses
论文作者
论文摘要
含铅眼镜的粘度对于制造过程至关重要，可以通过人工神经网络等算法来预测。 Sciglass数据库用于提供化学成分，温度和粘度的训练，验证和测试数据，以构建隐藏层的节点变化的人工神经网络。将使用培训数据和验证数据构建的最佳模型与来自文献的平均绝对误差和确定系数的统计学评估更好，并与文献进行了更好的统计评估，随后的灵敏度分析与文献一致。计算了偏度和峰度，并且与测试数据建立的最佳神经网络预测的值之间存在良好的相关性。
The viscosity of lead-containing glasses is of fundamental importance for the manufacturing process, and can be predicted by algorithms such as artificial neural networks. The SciGlass database was used to provide training, validation and test data of chemical composition, temperature and viscosity for the construction of artificial neural networks with node variation in the hidden layer. The best model built with training data and validation data was compared with 7 other models from the literature, demonstrating better statistical evaluations of mean absolute error and coefficient of determination to the test data, with subsequent sensitivity analysis in agreement with the literature. Skewness and kurtosis were calculated and there is a good correlation between the values predicted by the best neural network built with the test data.
