基于离散傅立叶变换的支持向量机光谱定量分析法  

Quantitative Analysis Method of Support Vector Machine Based on Discrete Fourier Transform

作　　者：王圣毫[1] 李智[1] 胡荣[2] 宋福圣[3] 王岩 

机构地区：[1]沈阳工程学院辽宁省电力仿真控制重点实验室,辽宁沈阳110136 [2]中国人民大学化学系,北京100872 [3]沈阳工程学院自动化学院,辽宁沈阳110136 [4]辽宁东方发电厂生技部,辽宁抚顺113000

出　　处：《分析测试学报》2014年第6期666-671,共6页Journal of Instrumental Analysis

基　　金：国家自然科学基金资助项目(61371200);辽宁省高等学校科学研究立项计划项目(2007T122)

摘　　要：研究了火电厂电煤煤粉的近红外光谱特征,提取了前3个主成分和前6个离散傅立叶变换(DFT)系数,结合主成分得分、马氏距离和偏最小二乘(PLS)交互验证方法剔除异常样本,并建立偏最小二乘回归(PLSR)、栅格支持向量机回归(G-SVR)、遗传算法支持向量机回归(GA-SVR)和粒子群算法支持向量机回归(PSO-SVR)等定量分析模型。结果表明,利用DFT系数作为PSO-SVR模型的输入变量,当其进化代数为300,种群规模为20,模型参数c1、c2为1.5,1.7时,性能最优,其中校正集相关系数(RC)为0.990,测试集相关系数(RP)为0.954,定标标准差(SEC)为0.366,测试标准差(SEP)为0.128。该方法准确可靠,已成功应用于近红外在线电煤发热量监测系统,并可推广用于其它较为复杂的近红外在线分析系统。The characteristics of the near infrared（ NIR） spectra from the electricity coal samples were investigated. During the whole process of the study,with principal components score,Mahalanobis distance and Partial Least Squares cross validation for picking out outliers,the first three principal components and six discrete Fourier transformation（ DFT） coefficients were obtained,and finally partial least squares regression（ PLSR）,grid-support vector regression（ G-SVR）,genetic algorithm-support vector regression（ GA-SVR） and particle swarm optimization-support Vector Regression（ PSO-SVR） quantitative analysis models were constructed. The results indicated that when the particle swarm optimization-support vector regression generation was 300,the population was20,when c1was 1. 5 and c2was 1. 7,the calibration correlation coefficient was 0. 990,the prediction correlation coefficient was 0. 954,the calibration standard error was 0. 366 and the prediction standard error was 0. 128. The method was accurate and reliable,and was applied in the near infrared electricity coal calorific value detection system,which could also be used in other extremely complex near infrared online systems.

关 键 词：近红外 支持向量机回归 离散傅立叶变换 煤发热量 定量分析模型 

分 类 号：O657.3[理学—分析化学]