基于ARM的不同参数含量聚醚的检测及识别  

Detection and Identification of Polyether with Different Parameters Based on ARM

作　　者：丁兴林 张宇林[2] 

机构地区：[1]淮阴工学院自动化学院,江苏淮安223003 [2]淮阳工学院电子信息工程学院,江苏淮安223003

出　　处：《仪表技术与传感器》2017年第3期102-105,共4页Instrument Technique and Sensor

基　　金：江苏省产学研项目(BY2016061-02)

摘　　要：为了提高聚醚后处理的自动化程度、聚醚参数的精度和识别不同参数含量的聚醚,采用ARM为控制核心,Wincc组态软件为上位机,对聚醚的黏度、pH值、色度、水分参数等进行检测,并选取了5种不同参数含量的聚醚样品,分别采用主成分分析法和BP神经网络对聚醚样品进行模式识别。试验结果表明:采用主成分分析法的第一主成分和第二主成分的累积贡献率达90.235 2%,识别效果良好;而BP神经网络经过多次训练后,识别率达到100%。In order to improve the degree of automation and the accuracy of polyether parameters and the identification of different parameters of poly,ARM was used as the control core,the Wincc configuration software was the host computer.The temperature,PH value,color,moisture content and so on were detected,five kinds of polyether samples with different parameters were selected,and the pattern recognition of polyether samples was performed by using principal component analysis and BP neural network respectively.The results show that the cumulative contribution rate of the first principal component and the seconed principal component of the principal component analysis method was 90.235 2%,and the recognition effect was good.And the recognition rate of BP neural network was 100% after several times of training.

关 键 词：聚醚后处理 ARM WINCC 识别 主成分分析 BP神经网络 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]