关键词: 粗差 , 加权 ,  最小二乘支持向量机 , 软测量

Abstract: In order to eliminate the influence of unavoidable outliers in soft sensor sample data on model's performance,a novel adaptive weighted least square support vector machine regression method(AWLS-SVM) was proposed.Firstly,in AWLS-SVM,least square support vector machine regression was employed for the sample data to develop model and obtain the sample datum fitting error.Then,according to the fitting error,the initial error weight was calculated.Secondly,the lever weight was defined according to the space distribution of the sample data.Finally,combining the error weights and lever weights,the adaptive sample weights were obtained via the proposed adaptive iterative method.Thus,the model was developed by AWLS-SVM with the adaptive sample weights.The simulation experiment results show that the outliers influence on the model's performance is eliminated in AWLS-SVM,and that the prediction performance is better than those of LS-SVM and radial basis function network method.Further,AWLS-SVM was applied to develop the soft sensor model for 4-carboxybenzaldehyde concentration in terephthalic acid,which is the most important intermediate product of p-xylene oxidation to terephthalic acid reaction.The satisfied result is obtained.onclick="SelectDisplayDiv('EnDivSummaryMore','EnDivSummaryHuanYuan');HideSpanDiv('EnDivSummaryMoreSummary')">

Key words: Outliers , Weighted , Least square support vector machine , Soft sensor