Hybrid modeling of microwave devices using multi-kernel support vector regression with prior knowledge

Abstract

This article proposes a support-vector hybrid modeling method of microwave devices when only a small number of measurements are available. In this method, a hybrid model of microwave device has been obtained by combining a coarse model and a support-vector model, where the coarse model is complemented by a support-vector model capable of correcting the difference between the measurements and the coarse model. The support-vector model was developed using a novel algorithm. In the algorithm, multi-kernel and prior knowledge from a calibrated simulator were incorporated into the framework of the linear programming support vector regression by utilizing multiple feature spaces and modifying the optimization formulation. The experimental results from two microwave devices show that the hybrid modeling can enhance the physical meaning of the support-vector model and improve the modeling accuracy for a small dataset, and that the proposed algorithm shows great potential in some applications where sufficient experimental data is difficult and costly to obtain, but the prior knowledge from a simulation model is available. The hybrid modeling is suited to a microwave computer-aided design tool or an automatic tuning robot. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:219–228, 2015.