中国烟草科学2016，Vol.37Issue(2)：65-70,6.DOI:10.13496/j.issn.1007-5119.2016.02.012
基于RBF神经网络建立库存烟叶香型的预测模型
The Establishment of Prediction Model of Inventory Tobacco Flavor Based on RBF Neural Network
摘要
Abstract
In order to establish the prediction model of inventory tobacco flavor, the authors analyze the samples of 2009-2011 inventory tobacco in China Tobacco Chuanyu Industrial Co., Ltd. by using the RBF neural network method. The results showed that there was difference of the content of chemical components between different tobacco flavors, sugar content in clean aroma type tobacco was significantly higher than the others, and chlorine content in clean aroma type tobacco was much lower than that of full-bodied type. The authors used principal component analysis to eliminate the chemical indicator collinear problem, and established prediction models based on RBF neural network of inventory tobacco flavor. The accuracy rate of the models was up to 90%. The sensitivity test showed that the clean aroma type tobacco model had the best sensitivity, the moderate type showed a lower sensitivity. Tobacco flavor can be predicted based on chemical components using the RBF neural network.关键词
库存烟叶/香型/主成分分析/RBF神经网络Key words
inventory tobacco/flavor/principal component analysis/RBF neural network分类
轻工纺织引用本文复制引用
周泽弘,曹淋海,王昌全,李启权,李冰,李珊..基于RBF神经网络建立库存烟叶香型的预测模型[J].中国烟草科学,2016,37(2):65-70,6.基金项目
四川省烟草公司重点项目"基于3S技术的四川烟区生态环境要素时空特征提取及应用"(SCYC201402006) （SCYC201402006）
四川省烟草公司重点项目"四川植烟土壤质量监测评价及退化阻控技术研究"(201202005) （201202005）
川渝中烟工业有限责任公司重点项目"公司烟叶原料品质数据库建设与应用研究"(12097) （12097）
