A New Weighted Least Square Support Vector Machine and Its Application in the Temperature Measurement of Texaco Slurry Gasifier

To satisfy the requirement of the online estimation of temperature for Texaco slurry gasifier, the outliers of the sampled data from Texaco slurry gasifier are divided into two categories, i.e., vertical outlier and leverage point. A new weighted least square support vector machine (WLS-SVM) is proposed to attenuate the two kinds of outliers. The parameters in WLS-SVM are decided by the optimal parameters of LS-SVM, which are further optimized by means of the generalization error of LS-SVM model. Simulation experiments based on test functions illustrates the effectiveness of the proposed method. Finally, the present method is applied to the soft sensor model for the temperature of Texaco slurry gasifier, and some satisfying results are obtained.