International Journal of RF and Microwave Computer-Aided Engineering · 2007 · 37 citations · 9 references
EngineeringComputer ScienceElectrical EngineeringMachine LearningMicroelectronicsMicrowave EngineeringDevice ModelingMicrowave Integrated CircuitsMicrowave Noise AnalysisMicrowave Semiconductor DevicesNeural NetworksSupport Vector MachinesBlack Box ModelingMicrowave Circuit ModelingMicrowave ModelingMicrowave Noise CharacterizationMicrowave TransistorsSmall-signal Device ModelingSmall-signal Noise AnalysisSupport Vector Classification
In this work, a support vector machines (SVM) model for the small-signal and noise behaviors of a microwave transistor is presented and compared with its artificial neural network (ANN) model. Convex optimization and generalization properties of SVM are applied to the black-box modeling of a microwave transistor. It has been shown that SVM has a high potential of accurate and efficient device modeling. This is verified by giving a worked example as compared with ANN which is another commonly used modeling technique. It can be concluded that hereafter SVM modeling is a strongly competitive approach against ANN modeling. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.
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The Nature of Statistical Learning
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