Abstract:
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.