{"title":"基于粒子群算法的射频功率晶体管超参数优化支持向量回归模型","authors":"Zhiwei Gao, Bo Liu, Giovanni Crupi, Jialin Cai","doi":"10.1002/jnm.70013","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>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.</p>\n </div>","PeriodicalId":50300,"journal":{"name":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","volume":"38 1","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hyperparameter Optimized SVR Model Based on Particle Swarm Algorithm for RF Power Transistors\",\"authors\":\"Zhiwei Gao, Bo Liu, Giovanni Crupi, Jialin Cai\",\"doi\":\"10.1002/jnm.70013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>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.</p>\\n </div>\",\"PeriodicalId\":50300,\"journal\":{\"name\":\"International Journal of Numerical Modelling-Electronic Networks Devices and Fields\",\"volume\":\"38 1\",\"pages\":\"\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Numerical Modelling-Electronic Networks Devices and Fields\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jnm.70013\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jnm.70013","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Hyperparameter Optimized SVR Model Based on Particle Swarm Algorithm for RF Power Transistors
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.
期刊介绍：
Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models.
The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics.
Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.
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