基于RBF神经网络建立库存烟叶香型的预测模型

为建立库存烟叶香型预测模型,采用RBF神经网络方法,对川渝中烟2009-2011年库存烟叶样品的香型特征进行了分析建模。结果表明,不同香型烟叶在化学成分含量上存在差异,清香型烟叶糖含量明显高于其他香型,氯含量远低于浓香型;采用主成分分析消除各化学指标共线问题,并建立基于RBF神经网络的库存烟叶香型预测模型,其准确率高达90%;灵敏度检验表明,清香型烟叶模型灵敏度为最优,中间香型灵敏度较低。证明利用RBF神经网络可以较好地对烟叶的常规化学成分进行烟叶香型预测。

The Establishment of Prediction Model of Inventory Tobacco Flavor Based on RBF Neural Network

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.