The nature of statistical learning theory
The nature of statistical learning theory
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Incorporating prior knowledge in support vector regression
Machine Learning
Support vector design of the microstrip lines
International Journal of RF and Microwave Computer-Aided Engineering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
International Journal of RF and Microwave Computer-Aided Engineering
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This article presents a support-vector modeling method for electromechanical coupling of microwave filter tuning in the case of the scarcity of experimental data available. This has been done for the purpose of establishing an accurate coupling model which can be used in an automatic tuning device of volume-producing filters. In the method, a coupling model that reveals the effect of mechanical structure on the filter electrical performance is established by using a proposed algorithm which can incorporate multi-kernel and prior knowledge into linear programming support vector regression (LPSVR). Some experiments from three microwave filters have been performed, and the results confirm the effectiveness of the support-vector modeling method. Moreover, the comparative results also show that the proposed multi-kernel prior knowledge LPSVR can improve the data-driven modeling accuracy of small dataset. The proposed algorithm show great potential in some problems where a sufficient experimental data is difficult and costly to obtain, but some prior knowledge data from a simulation model can be easily obtained. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013. © 2013 Wiley Periodicals, Inc.