杨佳文,曾台英.人工神经网络在不同蓄冷剂参数下保温性能评估的应用[J].包装工程,2023,44(15):175-183.
YANG Jia-wen,ZENG Tai-ying.Application of Artificial Neural Network in Evaluation of Thermal Insulation Performance under Different Parameters of Cool Storage Agent[J].Packaging Engineering,2023,44(15):175-183. |
| 人工神经网络在不同蓄冷剂参数下保温性能评估的应用 |
| Application of Artificial Neural Network in Evaluation of Thermal Insulation Performance under Different Parameters of Cool Storage Agent |
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| DOI：10.19554/j.cnki.1001-3563.2023.15.023 |
| 中文关键词: 冷链保温箱 蓄冷剂 人工神经网络 均方误差 |
| 英文关键词: cold chain incubator cold storage agent artificial neural network mean square error |
| 基金项目:高水平大学科建设医工交叉创新项目(10-22-309-501) |
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| 中文摘要: |
| 目的 利用不同人工神经网络算法预测不同蓄冷剂参数下冷链保温箱保温时间，以寻找最适合评估其保温性能的人工神经网络。方法 将实验数据以4∶1的比例分别随机分配训练、测试的样本，分别建立BPNN、RBFNN与GRNN这3种人工神经网络模型，并提出判定系数(R2)、平均绝对误差(MAE)与均方误差(MSE)这3个评价标准。通过算法获得保温时间预测值和评价标准具体值，并且利用随机漫步算法对性能最好的神经网络进行优化。结果 通过R2、MAE和MSE这3个神经网络评价标准以及保温时间的实际值与预测值的对比图，得出RBFNN神经网络的性能最佳、精度最准、拟合最好，它的R2远高于GRNN和BPNN神经网络的，并且MSE值和MAE值远低于GRNN和BPNN神经网络的，3个评价指标分别达到0.999 93、0.009 63和0.062 86。优化后的Random-Walk-RBFNN的性能进一步提高，R2提升了0.004%，MSE值、MAE值和运行时间分别下降了60.02%、34.20%和5.29%。结论 RBFNN神经网络各方面最为突出，更适合用于冷链保温箱保温性能评估，而优化后的Random-Walk-RBFNN性能更优，R2进一步提升，MSE值、MAE值和运行时间进一步下降，评估性能更好。 |
| 英文摘要: |
| The work aims to use different artificial neural network algorithms to predict the holding time of cold chain incubator under different cold storage agent parameters in order to find the most suitable artificial neural network for evaluating the thermal insulation performance. The experimental data were randomly assigned to the training and testing samples in a ratio of 4∶1, and three artificial neural network models, namely BPNN, RBFNN and GRNN, were established respectively. Three evaluation criteria, namely the decision coefficient R2, mean absolute error MAE and mean square error MSE, were proposed. The predicted value of holding time and the specific value of evaluation criteria were obtained by the algorithm, and the neural network with the best performance was optimized by the random walk algorithm. By comparing the three evaluation criteria of R2, MAE and MSE and the actual and predicted values of holding time, it was concluded that RBFNN neural network had the best performance, the most accurate precision and the best fitting. Its R2 was much higher than those of GRNN and BPNN neural networks, and MSE and MAE were much lower than those of GRNN and BPNN neural networks. The three evaluation indicators reached 0.999 93, 0.009 63 and 0.062 86, respectively. The performance of the optimized Random-Walk-RBFNN was further improved, R2 was increased by 0.004%, and MSE, MAE and running time were decreased by 60.02%, 34.20% and 5.29%, respectively. RBFNN neural network is the most outstanding in all aspects, which is more suitable for evaluating the thermal insulation performance of cold chain incubator. The optimized Random-Walk- RBFNN has better performance, improves R2, reduces MSE, MAE and running time, and achieves better evaluation performance. |
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