烟丝物理指标对其回弹特性的影响研究
Research on the influence of physical indicators of cut tobacco on its rebound characteristics
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摘要: 以5种牌号常规卷烟成品烟丝为研究对象,采用多重线性回归分析方法研究烟丝物理指标对其回弹特性的影响规律,并构建基于BP神经网络的烟丝回弹特性预测模型。结果表明:烟丝物理指标对烟丝回弹特性的影响程度从大到小依次为碎丝率>中丝率>长丝率>填充值>弹性>含水率,其中,中丝率、长丝率、弹性、含水率与烟丝回弹特性呈正相关关系,碎丝率、填充值与烟丝回弹特性呈负相关关系;所构建的BP神经网络预测模型,测试集预测值与真实值比对R2为0.965 7,总体模型精度达到98.10%,烟丝回弹特性预测值与实测值之间的差异较小,模型具有较高的预测准确性和可靠性,可用于烟丝回弹特性的精确估算。Abstract: Using 5 grades of conventional cigarette finished tobacco as the research object, multiple linear regression analysis method was used to study the influence of physical indicators of cut tobacco on its rebound characteristics, and a prediction model for tobacco rebound characteristics based on BP neural network was constructed. The results showed that the degree of influence of physical indicators of cut tobacco on the rebound characteristics of tobacco was ranked from large to small as follows: broken tobacco rate>medium tobacco rate>long tobacco rate>filling value>elasticity>moisture content. Among them, medium tobacco rate, long tobacco rate, elasticity, and moisture content were positively correlated with the rebound characteristics of tobacco, while broken tobacco rate and filling value were negatively correlated with the rebound characteristics of tobacco. In the constructed BP neural network prediction model, the comparison between the predicted value of the test set and the true value R2 was 0.965 7, the overall model accuracy reached 98.10% . The difference between the predicted and measured values of cut tobacco rebound characteristics was small, and the model had high prediction accuracy and reliability, which could be used for accurate estimation of cut tobacco rebound characteristics.
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