{"title":"改进电磁器件的神经网络灵敏度提取","authors":"D.A. Vieira, J. Vasconcelos, W. Caminhas","doi":"10.1109/CEFC-06.2006.1633090","DOIUrl":null,"url":null,"abstract":"This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of user's defined parameters, as, for instance, the number of neurons. Some results are presented considering the sensitivity extraction of a loudspeaker magnet assembly, and they show the effectiveness of the proposed approach","PeriodicalId":262549,"journal":{"name":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Neural Networks Sensitivity Extraction of Electromagnetic Devices\",\"authors\":\"D.A. Vieira, J. Vasconcelos, W. Caminhas\",\"doi\":\"10.1109/CEFC-06.2006.1633090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of user's defined parameters, as, for instance, the number of neurons. Some results are presented considering the sensitivity extraction of a loudspeaker magnet assembly, and they show the effectiveness of the proposed approach\",\"PeriodicalId\":262549,\"journal\":{\"name\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEFC-06.2006.1633090\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 12th Biennial IEEE Conference on Electromagnetic Field Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEFC-06.2006.1633090","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improving Neural Networks Sensitivity Extraction of Electromagnetic Devices
This paper applies the parallel layer perceptron network trained with the minimum gradient method (PLP-MGM) to the problem of sensitivity extraction of electromagnetic devices. The networks trained with the MGM are less dependent of user's defined parameters, as, for instance, the number of neurons. Some results are presented considering the sensitivity extraction of a loudspeaker magnet assembly, and they show the effectiveness of the proposed approach
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