N. K. Saxena, R. Verma, P. Pourush, Nitendar Kumar
{"title":"铁氧体矩形贴片天线开关性的遗传算法分析","authors":"N. K. Saxena, R. Verma, P. Pourush, Nitendar Kumar","doi":"10.9790/0661-1903066165","DOIUrl":null,"url":null,"abstract":"The application of Genetic Algorithm (GA) to analyze / optimize the switching behavior of magnetically biased switchable microstrip antenna, fabricated on ferrite substrate, is reported. In this work, GA has been applied to optimize extraordinary wave propagation constant (Ke) which is mainly responsible for the switchability of antenna. The wave propagation constant becomes zero or negative under proper magnetic biasing which resist the antenna as radiator without a mechanical maneuvering. The fitness functions for the GA program have been developed using mathematical formulation based on nonreciprocal approach of ferrite substrate under external magnetic field. The computed results are in good agreement with the results obtained experimentally and trained artificial neural network analysis. In this ANN training Radial Basis Function (RBF) networks is used. All programming related to genetic algorithm and ANN analysis performed by MatLab 7.1.","PeriodicalId":91890,"journal":{"name":"IOSR journal of computer engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GA Analysis of Switchability of Ferrite Rectangular Patch Antenna\",\"authors\":\"N. K. Saxena, R. Verma, P. Pourush, Nitendar Kumar\",\"doi\":\"10.9790/0661-1903066165\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application of Genetic Algorithm (GA) to analyze / optimize the switching behavior of magnetically biased switchable microstrip antenna, fabricated on ferrite substrate, is reported. In this work, GA has been applied to optimize extraordinary wave propagation constant (Ke) which is mainly responsible for the switchability of antenna. The wave propagation constant becomes zero or negative under proper magnetic biasing which resist the antenna as radiator without a mechanical maneuvering. The fitness functions for the GA program have been developed using mathematical formulation based on nonreciprocal approach of ferrite substrate under external magnetic field. The computed results are in good agreement with the results obtained experimentally and trained artificial neural network analysis. In this ANN training Radial Basis Function (RBF) networks is used. All programming related to genetic algorithm and ANN analysis performed by MatLab 7.1.\",\"PeriodicalId\":91890,\"journal\":{\"name\":\"IOSR journal of computer engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IOSR journal of computer engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9790/0661-1903066165\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOSR journal of computer engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9790/0661-1903066165","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
GA Analysis of Switchability of Ferrite Rectangular Patch Antenna
The application of Genetic Algorithm (GA) to analyze / optimize the switching behavior of magnetically biased switchable microstrip antenna, fabricated on ferrite substrate, is reported. In this work, GA has been applied to optimize extraordinary wave propagation constant (Ke) which is mainly responsible for the switchability of antenna. The wave propagation constant becomes zero or negative under proper magnetic biasing which resist the antenna as radiator without a mechanical maneuvering. The fitness functions for the GA program have been developed using mathematical formulation based on nonreciprocal approach of ferrite substrate under external magnetic field. The computed results are in good agreement with the results obtained experimentally and trained artificial neural network analysis. In this ANN training Radial Basis Function (RBF) networks is used. All programming related to genetic algorithm and ANN analysis performed by MatLab 7.1.