{"title":"基于神经网络的微带滤波器设计","authors":"G. Tomar, V. Kushwah, Shilpam Saxena","doi":"10.1109/ICCSN.2010.103","DOIUrl":null,"url":null,"abstract":"This paper is intended to present the design procedure for microstrip filters using artificial neural networks to provide better resolution and cutoff characteristics. The design concept has been evaluated with specific design of Low pass filters at the application specific frequencies. In this design procedure, synthesis is defined as the forward side and then analysis as the reverse side of the problem. To achieve results, the neural network is employed as a tool in design of microstrip filters. Neural network Training algorithms are used to train the samples so that error can be minimized and sharpness of slop is improved.","PeriodicalId":255246,"journal":{"name":"2010 Second International Conference on Communication Software and Networks","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Design of Microstrip Filters Using Neural Network\",\"authors\":\"G. Tomar, V. Kushwah, Shilpam Saxena\",\"doi\":\"10.1109/ICCSN.2010.103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is intended to present the design procedure for microstrip filters using artificial neural networks to provide better resolution and cutoff characteristics. The design concept has been evaluated with specific design of Low pass filters at the application specific frequencies. In this design procedure, synthesis is defined as the forward side and then analysis as the reverse side of the problem. To achieve results, the neural network is employed as a tool in design of microstrip filters. Neural network Training algorithms are used to train the samples so that error can be minimized and sharpness of slop is improved.\",\"PeriodicalId\":255246,\"journal\":{\"name\":\"2010 Second International Conference on Communication Software and Networks\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Second International Conference on Communication Software and Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSN.2010.103\",\"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 Second International Conference on Communication Software and Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSN.2010.103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper is intended to present the design procedure for microstrip filters using artificial neural networks to provide better resolution and cutoff characteristics. The design concept has been evaluated with specific design of Low pass filters at the application specific frequencies. In this design procedure, synthesis is defined as the forward side and then analysis as the reverse side of the problem. To achieve results, the neural network is employed as a tool in design of microstrip filters. Neural network Training algorithms are used to train the samples so that error can be minimized and sharpness of slop is improved.