Machine Learning for Complex EMI Prediction, Optimization and Localization

被引：0

作者：

Jin, Hang ^{[1
]}

Zhang, Le ^{[1
]}

Ma, Han-Zhi ^{[1
]}

Yang, Si-Chen ^{[1
]}

Yang, Xiao-Li ^{[1
]}

Li, Er-Ping ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Key Lab Adv Micro Nano Elect Devices & Smart Syst, Hangzhou 310027, Zhejiang, Peoples R China

来源：

2017 IEEE ELECTRICAL DESIGN OF ADVANCED PACKAGING AND SYSTEMS SYMPOSIUM (EDAPS) | 2017年

基金：

中国国家自然科学基金;

关键词：

Artificial Intelligence; Bayesian optimization algorithm; deep neural network; electromagnetic interference; machine learning; sources localization; NEURAL-NETWORKS; DESIGN;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The electromagnetic interference (EMI) problem of extra-high speed electronic devices and systems is becoming more complex with an increase of operating frequency. The conventional analysis and design methods could not cope with the current EMI problems. Advanced analysis and design methods are desired. Deep neural network (DNN) and Bayesian optimization algorithm (BOA) based on machine learning are utilized in prediction of EMI radiation, optimization of design parameters and localization of EMI sources. The feasibility of DNN and BOA is investigated and validated. The steps of using DNN and BOA are proposed in the paper.

引用

页数：3

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