可拓神经网络模式识别对成品油的鉴别与测量

燃油存在“消耗量大”、“相对低质”、“前端缺少清洁”、“末端排放缺乏控制”四大问题, 我国的空气污染60%以上来自煤和油的燃烧，雾霾问题很大程度上取决于能源问题。快速准确地实现汽油、柴油、煤油等成品油的鉴别与测量，对于实施空气污染监测及治理具有重要意义。在精确地表征成品油种类信息的基础上，为了提高网络模型的识别效率，采用主成分分析方法将高维空间进行降维处理。对最常用的三维荧光光谱基于激发-发射矩阵(excitation-emission matrix, EEM)数据进行主成分分析以提取更精细、更深层的特征参量。分类过程中应用交叉验证的方法避免发生“过拟合”现象。设计鉴别和测量双重处理的神经网络，将神经网络模式识别结果反馈到浓度网络的输入端，与相对斜率、综合本底参数、相对荧光强度一起测量相应种类的浓度输出，利用可拓神经网络模式识别技术实现成品油的鉴别与测量。应用可拓神经网络方法实现成品油种类模式识别的平均识别率达到0.99，浓度平均回收率为0.95。模式识别平均耗时为2.5 s，仅为PARAFAC模型分析方法的48.5%。该方法显著提高了运算速度，且应用效果理想。需要指出的是，在分析诸如成品油、茶叶、农药等成分复杂的混合物时，应针对具体待测物制作相应的校正样本，用以确保分析的准确性与精度。

Study on Refined Oil Identification and Measurement Based on the Extension Neural Network Pattern Recognition

There are four major problems related to fuel consumption,“large consumption”,“low quality”,“lack of front-end clean”and “lack of end emission control”,which needs to address urgently for our country.More than 60 percent of the air pol-lution is due to the burning of coal and oil in our country,so the haze problem depends on how much we can deal with energy is-sues.We should achieve the identification and measurement of gasoline,diesel,kerosene and other refined oil products rapidly and accurately,which is important for the implementation of air pollution monitoring and controlling.in order to characterize the type information of the refined oil accurately and to improve the efficiency of the network model identification,it is effective to use principal component analysis method which could achieve the data dimension reductionwhile reducing the complexity of the problem.With principal component analysis of the most commonly used three-dimensional fluorescence spectra based on excita-tion-emission matrix (Excitation-Emission Matrix,EEM)data,we could obtain finer,deeper characteristic parameters.During the process of classification,it could avoid the“over-fitting”phenomenon because of the application of the cross-validation meth-od,A neural network capable of both qualitative and quantitative analysis is designed.The neural network pattern recognition result becomes feedback to the input of the concentration network,together with the relative slope,the comprehensive back-ground parameters,and the relative fluorescence intensity,we could achieve the measurement of the concentration of the corre-sponding types,then use the extension neural network pattern recognition technology to achieve identification and measurement of kerosene,diesel,gasoline and other refined oil products.The results of the study show that the average recognition rate rea-ches 0.99,the average recovery rate of concentration reaches 0.95,the average time of pattern recognition is 2.5 seconds and this time is 48.5% of the time used by PARAFAC model analysis method.The method significantly improves the operation speed with ideal application effect .It should be pointed out that,in order to ensure the accuracy and precision of the analysis, we should make corresponding calibration samples for specific analytes in terms of the analysis of complex mixtures such as re-fined oil,pesticides,tea,etc.