基于bp神经网络的GaN hemt非线性等效电路模型

2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC) Pub Date : 2019-06-01 DOI:10.1109/EDSSC.2019.8754355

Manli Xue, Lu Sun, Shuo Wang, Peipei Liang, Xiaolong Chen, F. Nian

{"title":"基于bp神经网络的GaN hemt非线性等效电路模型","authors":"Manli Xue, Lu Sun, Shuo Wang, Peipei Liang, Xiaolong Chen, F. Nian","doi":"10.1109/EDSSC.2019.8754355","DOIUrl":null,"url":null,"abstract":"A Back-Propagation Neural Network (BPNN) nonlinear model of $GaN$ HEMT is proposed, which can adaptively fit the nonlinear parameter relationship, and reduce computation. The network weights are obtained automatically, and then the nonlinear mapping relation is determined. The comparison between BPNN model and test data proves the good consistency.","PeriodicalId":183887,"journal":{"name":"2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Nonlinear Equivalent Circuit Model Base on BPNN for GaN HEMTs\",\"authors\":\"Manli Xue, Lu Sun, Shuo Wang, Peipei Liang, Xiaolong Chen, F. Nian\",\"doi\":\"10.1109/EDSSC.2019.8754355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A Back-Propagation Neural Network (BPNN) nonlinear model of $GaN$ HEMT is proposed, which can adaptively fit the nonlinear parameter relationship, and reduce computation. The network weights are obtained automatically, and then the nonlinear mapping relation is determined. The comparison between BPNN model and test data proves the good consistency.\",\"PeriodicalId\":183887,\"journal\":{\"name\":\"2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EDSSC.2019.8754355\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDSSC.2019.8754355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

提出了一种反向传播神经网络(BPNN)非线性GaN - HEMT模型，该模型能自适应拟合非线性参数关系，减少了计算量。自动获得网络权值，进而确定网络的非线性映射关系。BPNN模型与测试数据的对比证明了该模型具有良好的一致性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Nonlinear Equivalent Circuit Model Base on BPNN for GaN HEMTs

A Back-Propagation Neural Network (BPNN) nonlinear model of $GaN$ HEMT is proposed, which can adaptively fit the nonlinear parameter relationship, and reduce computation. The network weights are obtained automatically, and then the nonlinear mapping relation is determined. The comparison between BPNN model and test data proves the good consistency.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)

自引率

0.00%

发文量