基于PCA-BP的中药提取率的软测量

The soft measurement of Chinese medicine's extraction rate based on PCA-BP

利用超声波萃取金银花中的绿原酸。依据工艺机理和操作经验,从提取过程中初选了数学模型所需的输入变量。对萃取过程中的提取率,利用主元分析方法PCA对输入变量进行主元分解,消除了输入变量之间的线性相关性,再利用BP神经网络进行数学建模,仿真结果表明:利用PCA法对输入数据进行处理后,与单独使用BP算法的建模结果相比较,该方法具有较快的训练速率和较高预测精度,提高了对中药萃取过程中提取率的在线软测量精度。

Using ultrasonic to extract chlorogenic acid from honeysuckle. According to technical mechanics and operating experience, selecting the initial input parameters of mathematical model from the extracting process, and using PCA to analyze the data, removing the correlation among them, and then using BP to build the mathematical model. The results of PCA using and the results only using BP are compared, and found that using principal component analysis （PCA） on the decomposition of data before treatment, and the training error was smaller, the training speed was faster. This method improved the accuracy of the mathematical model.