Data-driven modeling and optimization for cavity filters using linear programming support vector regression

摘要

This paper presents a data-driven modeling and optimization method for cavity filters, according to a limited amount of measurement data. In the method, a model that reveals the effect of mechanical structure on electrical performance of cavity filters is firstly developed by an improved algorithm, which can increase the modeling accuracy of small data set by incorporating multi-kernel and prior knowledge into the framework of linear programming support vector regression. Then, an approach to optimize the structure of cavity filters is formulated by using the developed data-based model, and the obtained results can assist the fabrication of the same filter in the future. Some experiments from a synthetic example and a practical application of cavity filter have been carried out, and the experimental results confirm the effectiveness of the method. The model is particularly suited to a computer-aided manufacturing of volume-producing filters, and the proposed algorithm shows great potential in some applications where the experimental data are very few and the prior knowledge is available.