Local Parameter Optimization of LSSVM for Industrial Soft Sensing With Big Data and Cloud Implementation

Due to the advantages of high prediction accuracy, least squares support vector machine (LSSVM) has been widely utilized for soft sensor developments in industrial processes. The hyper-parameters of LSSVM are often determined by minimizing the predicted error of validation set based on the intelligent optimization algorithm, which may lead to excessive optimization and model overfitting when validation set are selected improperly. Meanwhile, online parameters optimization is difficult to implement, which results in poor effect of local modeling. This paper proposes UMDA-LOS-LSSVM that is a LSSVM with parameters optimization in local objective set (LOS-LSSVM) by univariate marginal distribution algorithm (UMDA) based on the idea of local modeling. First, the local objective set is extracted in the candidate set based on the testing samples. Then, UMDA is utilized for minimize the predicted error of the objective set and provides the optimized parameters. Finally, training and testing of LSSVM are carried out based on the optimal parameters. In addition, this paper provides the distributed parallel form of the proposed method, which can be used for big data modeling and soft sensor development. The proposed method is applied in a CO2 absorbing column unit to estimate the residual CO2 content, which is implemented through an industrial big data distributed analytics platform. The results show a significant improvement of proposed method based soft sensor, compared to traditional methods.