{"title":"电磁逆散射与多层感知器:面向埋藏层性质的自动重建","authors":"S. Caorsi, M. Stasolla","doi":"10.1109/URSI-EMTS.2010.5637142","DOIUrl":null,"url":null,"abstract":"The goal of this paper is a preliminary robustness assessment of a recently published ANN-based algorithm for the evaluation of subsurface layers' properties. In particular, the analysis will focus on the dependency of overall performances on the training set dimensions and the neural networks capabilities of managing numerical errors.","PeriodicalId":404116,"journal":{"name":"2010 URSI International Symposium on Electromagnetic Theory","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"E.M. Inverse Scattering and Multi-Layer Perceptrons: Towards the automatic reconstruction of buried layers' properties\",\"authors\":\"S. Caorsi, M. Stasolla\",\"doi\":\"10.1109/URSI-EMTS.2010.5637142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The goal of this paper is a preliminary robustness assessment of a recently published ANN-based algorithm for the evaluation of subsurface layers' properties. In particular, the analysis will focus on the dependency of overall performances on the training set dimensions and the neural networks capabilities of managing numerical errors.\",\"PeriodicalId\":404116,\"journal\":{\"name\":\"2010 URSI International Symposium on Electromagnetic Theory\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 URSI International Symposium on Electromagnetic Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/URSI-EMTS.2010.5637142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 URSI International Symposium on Electromagnetic Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URSI-EMTS.2010.5637142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
E.M. Inverse Scattering and Multi-Layer Perceptrons: Towards the automatic reconstruction of buried layers' properties
The goal of this paper is a preliminary robustness assessment of a recently published ANN-based algorithm for the evaluation of subsurface layers' properties. In particular, the analysis will focus on the dependency of overall performances on the training set dimensions and the neural networks capabilities of managing numerical errors.
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