{"title":"基于GRNN的微波功率器件x参数建模","authors":"Q. Lin, Xiao-zheng Wang, Haifeng Wu","doi":"10.1109/MAPE53743.2022.9935173","DOIUrl":null,"url":null,"abstract":"The nonlinear parameter extraction for microwave power devices has the problems of low efficiency and high complexity. An X-parameter modeling based on general regression neural network (GRNN) is proposed in this paper. It is demonstrated that the GRNN model can realize the fit rate of 96.0 % and the mean-square error (MSE) of 0.0013. Consequently, the nonlinear characteristics for transistor can be accurately characterized by GRNN model.","PeriodicalId":442568,"journal":{"name":"2022 IEEE 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"X-Parameter Modeling for Microwave Power Device Based on GRNN\",\"authors\":\"Q. Lin, Xiao-zheng Wang, Haifeng Wu\",\"doi\":\"10.1109/MAPE53743.2022.9935173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The nonlinear parameter extraction for microwave power devices has the problems of low efficiency and high complexity. An X-parameter modeling based on general regression neural network (GRNN) is proposed in this paper. It is demonstrated that the GRNN model can realize the fit rate of 96.0 % and the mean-square error (MSE) of 0.0013. Consequently, the nonlinear characteristics for transistor can be accurately characterized by GRNN model.\",\"PeriodicalId\":442568,\"journal\":{\"name\":\"2022 IEEE 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MAPE53743.2022.9935173\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 9th International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MAPE53743.2022.9935173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
X-Parameter Modeling for Microwave Power Device Based on GRNN
The nonlinear parameter extraction for microwave power devices has the problems of low efficiency and high complexity. An X-parameter modeling based on general regression neural network (GRNN) is proposed in this paper. It is demonstrated that the GRNN model can realize the fit rate of 96.0 % and the mean-square error (MSE) of 0.0013. Consequently, the nonlinear characteristics for transistor can be accurately characterized by GRNN model.
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