A novel approach for optimizing the hyperparameters of a support vector regression (SVR) model is presented for radio frequency (RF) power transistors. In standard SVR models, hyperparameters are enhanced using grid search optimization (GSO), which can be inefficient. In this study, particle swarm optimization (PSO) is introduced as a method for optimizing hyperparameters in a SVR model that increases the model optimization efficiency significantly in comparison with GSO while maintaining a high level of performance. To verify the accuracy and effectiveness of the model, a 10-W GaN power transistor produced by Wolfspeed is used. In comparison to the existing GSO-SVR model, the proposed PSO-SVR model demonstrates superior performance and efficiency.
Hyperparameter Optimized SVR Model Based on Particle Swarm Algorithm for RF Power Transistors
Crupi G.Penultimo
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2025-01-01
Abstract
A novel approach for optimizing the hyperparameters of a support vector regression (SVR) model is presented for radio frequency (RF) power transistors. In standard SVR models, hyperparameters are enhanced using grid search optimization (GSO), which can be inefficient. In this study, particle swarm optimization (PSO) is introduced as a method for optimizing hyperparameters in a SVR model that increases the model optimization efficiency significantly in comparison with GSO while maintaining a high level of performance. To verify the accuracy and effectiveness of the model, a 10-W GaN power transistor produced by Wolfspeed is used. In comparison to the existing GSO-SVR model, the proposed PSO-SVR model demonstrates superior performance and efficiency.Pubblicazioni consigliate
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