Incorporating prior knowledge and multi-kernel into linear programming support vector regression

This paper proposes a multi-kernel linear program support vector regression with prior knowledge in order to obtain an accurate data-driven model in the case of an insufficient amount of measured data. In the algorithm, multiple feature spaces have been utilized to incorporate multi-kernel functions into the framework of linear programming support vector regression (LPSVR), and then the prior knowledge which may be exact or biased from a calibrated physical simulator has also been incorporated into LPSVR by modifying optimization formulations. Moreover, a strategy of parameter selections for the proposed algorithm has been presented to facilitate practical applications. Some experiments from a synthetic example, a microstrip antenna and six-pole microwave filter have been carried out, and the experimental results show that the proposed algorithm can obtain a satisfactory data-based model in the case of the scarcity of measured data. The proposed algorithm shows great potentialities in some applications where the experimental data are insufficient for an accurate data-driven model and the prior knowledge from a calibrated physical simulator of practical applications is available.