NDLI: RBF model of microwave filter using PDGS with defected rectangles

Content Provider	IEEE Xplore Digital Library
Author	Jin Taobin Jin Jie Yao Ruipu Li Kejia Zhang Yizhen
Copyright Year	2010
Description	Author affiliation: School of Electronic Information Engineering, Tianjin University, 300072, China (Jin Jie; Li Kejia) \|\| School of Information Engineering, Tianjin University of Commerce, 300134, China (Jin Taobin; Yao Ruipu; Zhang Yizhen)
Abstract	Microwave filter is the kind of device which can separate different signals within a certain band of microwave frequency, widely used in microwave communication system. Periodic defected ground structures (PDGS) with defected rectangles have excellent filter properties when periodic unit amounts and structure sizes meet appropriate conditions. According to artificial neural network (ANN) theory, radial basis function (RBF) model of PDGS with etched rectangles is developed for the first time on the basis of FDTD analysis in this paper. Periodic unit amounts, the structure sizes of PDGS and the frequency are defined as the input samples of the RBF, transmission coefficient (S21) are defined as the output samples. Transmission coefficient of PDGS at any arbitrary parameters including periodic unit amounts, structure sizes and frequency within training values range can be obtained quickly from RBF model after the RBF has been successfully trained with improved Gaussian algorithm. Finally, RBF model has been approved by FDTD results. It is also showed that RBF model is very effective, which will provide powerful approach for the precise analysis and quick design of microwave filter using PDGS with defected rectangles.
Starting Page	519
Ending Page	522
File Size	269754
Page Count	4
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training S21 PDGS Artificial neural networks RBF Time domain analysis microwave filter Filtering theory Microwave filters FDTD Finite difference methods Periodic structures
Content Type	Text
Resource Type	Article

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