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Intelligent model of microwave low-pass filter using PDGS with defected rectangles

机译:带有缺陷矩形的PDGS微波低通滤波器的智能模型

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Microwave low-pass filter (LPF) is the kind of device which can separate different signals within microwave frequency range, widely used in microwave communication system. Periodic defected ground structures (PDGS) with defected rectangles has excellent low-pass properties when periodic unit amounts and structure sizes meet definite conditions. In this paper, intelligent model of PDGS-LPF with etched rectangles is developed for the first time on the basis of FDTD analysis. Periodic unit amounts, the structure sizes of PDGS and the frequency are defined as the input samples of the model, and the parameters of transmission coefficient (S21) are defined as the output samples according to artificial neural network (ANN) theory. Transmission coefficient of PDGS at any arbitrary periodic unit amounts, any arbitrary structure sizes and any arbitrary frequency within training values range can be obtained quickly from intelligent model after the ANN has been successfully trained with the Bayesian Regularization algorithm. Finally, intelligent model has been approved by FDTD results. It is also showed that intelligent model is very effective, which will provide powerful approach for the precise analysis and quick design of microwave low-pass filter using PDGS with defected rectangles.
机译:微波低通滤波器(LPF)是一种可以在微波频率范围内分离不同信号的设备,广泛用于微波通信系统中。当周期性单位数量和结构尺寸满足确定条件时,带有缺陷矩形的周期性缺陷地面结构(PDGS)具有出色的低通特性。本文在FDTD分析的基础上,首次建立了带有矩形矩形的PDGS-LPF智能模型。根据人工神经网络(ANN)理论,将周期单位量,PDGS的结构尺寸和频率定义为模型的输入样本,并将传输系数(S21)的参数定义为输出样本。在使用贝叶斯正则化算法成功训练ANN之后,可以从智能模型中快速获得训练值范围内任何任意周期性单位量,任意结构尺寸和任意频率下PDGS的传输系数。最后,智能模型已被FDTD结果所认可。结果表明,智能模型是非常有效的,它将为带有缺陷矩形的PDGS的微波低通滤波器的精确分析和快速设计提供有力的方法。

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