{"title":"基于Mamdani模糊推理系统的智能天线分类方法","authors":"Jitu Prakash Dhar;Eisuke Nishiyama;Ichihiko Toyoda","doi":"10.23919/comex.2024XBL0195","DOIUrl":null,"url":null,"abstract":"Proper antenna classification is required for complex systems including specific application and performance parameters such as resonance frequency, gain, and reflection coefficient because conventional mathematical decision-making models are not well-suited for evaluating these systems. This study proposes an antenna classification method based on Mamdani Fuzzy Inference System (FIS) to determine the appropriate antenna type for those requirements. The performance parameters of five types of antennas are evaluated to construct the configuration of the FIS. The FIS responses for unseen requirements verify the model's performance showing its classification capability for new data.","PeriodicalId":54101,"journal":{"name":"IEICE Communications Express","volume":"14 3","pages":"111-114"},"PeriodicalIF":0.3000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856830","citationCount":"0","resultStr":"{\"title\":\"An Intelligent Antenna Classification Method Using Mamdani Fuzzy Inference System\",\"authors\":\"Jitu Prakash Dhar;Eisuke Nishiyama;Ichihiko Toyoda\",\"doi\":\"10.23919/comex.2024XBL0195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Proper antenna classification is required for complex systems including specific application and performance parameters such as resonance frequency, gain, and reflection coefficient because conventional mathematical decision-making models are not well-suited for evaluating these systems. This study proposes an antenna classification method based on Mamdani Fuzzy Inference System (FIS) to determine the appropriate antenna type for those requirements. The performance parameters of five types of antennas are evaluated to construct the configuration of the FIS. The FIS responses for unseen requirements verify the model's performance showing its classification capability for new data.\",\"PeriodicalId\":54101,\"journal\":{\"name\":\"IEICE Communications Express\",\"volume\":\"14 3\",\"pages\":\"111-114\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2025-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856830\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEICE Communications Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10856830/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEICE Communications Express","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10856830/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
An Intelligent Antenna Classification Method Using Mamdani Fuzzy Inference System
Proper antenna classification is required for complex systems including specific application and performance parameters such as resonance frequency, gain, and reflection coefficient because conventional mathematical decision-making models are not well-suited for evaluating these systems. This study proposes an antenna classification method based on Mamdani Fuzzy Inference System (FIS) to determine the appropriate antenna type for those requirements. The performance parameters of five types of antennas are evaluated to construct the configuration of the FIS. The FIS responses for unseen requirements verify the model's performance showing its classification capability for new data.
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