The nature of statistical learning theory
The nature of statistical learning theory
Signal-noise support vector model of a microwave transistor: Research Articles
International Journal of RF and Microwave Computer-Aided Engineering
Support vector design of the microstrip lines
International Journal of RF and Microwave Computer-Aided Engineering
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In this paper, we proposed an efficient knowledge-based support vector regression machine (SVRM) method to build synthesis models of the transmission lines for the microwave integrated circuits, with the highest possible accuracy using the fewest accurate data. This method is based comprehensively on the powerful generalization capability of support vector machine (SVM) over other classical optimization techniques; especially its working principle based on the small sample statistical learning theory is utilized in lessening the need for the accurate training and validation data together with the human time. Thus, synthesis models as fast as the coarse models and at the same time as accurate as the fine models are obtained for the RF/microwave planar transmission lines. Since the method employs the reverse relations between the analysis and synthesis processes, therefore firstly general definitions of analysis and synthesis processes are made for the RF/microwave planar transmission lines. Then the synthesis data are obtained by reversing the analysis data according to these definitions, where analysis process may be based on either the analytical formulation or empirical (coarse) formulas. Thereafter, generation process of the fine support vector (SV) expansion for synthesis from the coarse SVs is put forward in the form of block diagrams, depending on type of the analysis processes. Finally, the proposed knowledge-based support vector method are demonstrated by the two typical worked examples, representing the typical analysis processes which belong to the commonly used transmission lines, conductor backed coplanar waveguides with upper shielding and microstrip lines. Besides, artificial neural network (ANN)s are employed also in modeling as a competent regressor and it is also verified that only SVs would be sufficient to be used in training ANN models. Success of the method and performances of the resulted synthesis models are presented as compared to each other and the conventional ones.