人工ニューラルネットワークを用いた平板層状媒質の比誘電率分布推定に関する基礎研究

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2023/09/01

タイトル(英語): Estimating Permittivity Distribution of Flat-layered Media with Non-uniform Thickness by using Artificial Neural Network

著者名: 孫　啓瑾（福岡大学），圓谷　友紀（福岡大学），孟　志奇（福岡大学）

著者名(英語): Qijin Sun (Fukuoka University), Tomonori Tsuburaya (Fukuoka University), Zhiqi Meng (Fukuoka University)

キーワード: 層状媒質，電波散乱，誘電率分布の推定，人工ニューラルネットワーク　　layered medium，electromagnetic wave scattering，permittivity distribution estimation，artificial neural network

要約(英語): Artificial Neural Network (ANN) has achieved great success in many fields, such as image and voice recognition. Recently, ANN is also applied to solve inverse scattering problems, because of the advantages that it is able to produce estimation results in real time and without local minimum problems, compared to optimization techniques. However, in the problem of estimating the permittivity distribution of a layered medium from the information of incident and scattered waves, as the number of layers increases, the possible combinations of permittivity become enormous, making it difficult to train an ANN. On the other hand, the performance is not good enough when using ANN to recognize both the permittivity and thickness of each layer from the scattered wave information, because the scattered wave is affected by both of them. In this paper, we propose new data-preprocessing techniques to address these issues, and the ANN-based estimation obtained good accuracy even when the observed data include some noise.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.143 No.9 （2023）

本誌掲載ページ: 284-291 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejfms/143/9/143_284/_article/-char/ja/