{"title":"人工神经网络在电磁兼容中的应用","authors":"Felix Burghardt, Reyno Garbe","doi":"10.1109/EMCSI.2018.8495246","DOIUrl":null,"url":null,"abstract":"Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.","PeriodicalId":120342,"journal":{"name":"2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Introduction of Artificial Neural Networks in EMC\",\"authors\":\"Felix Burghardt, Reyno Garbe\",\"doi\":\"10.1109/EMCSI.2018.8495246\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.\",\"PeriodicalId\":120342,\"journal\":{\"name\":\"2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMCSI.2018.8495246\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Symposium on Electromagnetic Compatibility, Signal Integrity and Power Integrity (EMC, SI & PI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMCSI.2018.8495246","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electromagnetic examinations are usually very expensive. Every simulation needs time for computation and every measurement needs time for preparation. In addition, similar results are generally expected when examining similar objects. If the relation between the differences of investigated objects and their results could be found, a prediction on objects which are not yet examined would be possible. In this paper, a method based on artificial neural networks will be presented, with which a prediction of simulation results of similar objects is possible.