{"title":"人工神经网络在电磁参数化建模与优化中的研究进展","authors":"L. Ma, Jianan Zhang, Shuxia Yan, Qijun Zhang","doi":"10.23919/eucap53622.2022.9769286","DOIUrl":null,"url":null,"abstract":"This paper reviews the recent advances in artificial neural networks (ANN) for electromagnetic (EM) parameterized modeling and optimization. As an advanced ANN-based EM parameterized modeling and optimization technique, the neurotransfer function (neuro-TF) is discussed further in this paper. The trained neuro-TF parameterized models can be further used for EM design optimization with repetitive geometrical variations.","PeriodicalId":228461,"journal":{"name":"2022 16th European Conference on Antennas and Propagation (EuCAP)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Recent Advances in Artificial neural networks for EM parameterized modeling and optimization\",\"authors\":\"L. Ma, Jianan Zhang, Shuxia Yan, Qijun Zhang\",\"doi\":\"10.23919/eucap53622.2022.9769286\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper reviews the recent advances in artificial neural networks (ANN) for electromagnetic (EM) parameterized modeling and optimization. As an advanced ANN-based EM parameterized modeling and optimization technique, the neurotransfer function (neuro-TF) is discussed further in this paper. The trained neuro-TF parameterized models can be further used for EM design optimization with repetitive geometrical variations.\",\"PeriodicalId\":228461,\"journal\":{\"name\":\"2022 16th European Conference on Antennas and Propagation (EuCAP)\",\"volume\":\"74 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 16th European Conference on Antennas and Propagation (EuCAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/eucap53622.2022.9769286\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 16th European Conference on Antennas and Propagation (EuCAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/eucap53622.2022.9769286","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent Advances in Artificial neural networks for EM parameterized modeling and optimization
This paper reviews the recent advances in artificial neural networks (ANN) for electromagnetic (EM) parameterized modeling and optimization. As an advanced ANN-based EM parameterized modeling and optimization technique, the neurotransfer function (neuro-TF) is discussed further in this paper. The trained neuro-TF parameterized models can be further used for EM design optimization with repetitive geometrical variations.
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