The BP Neural Networks Model of Combinatorial Nonperiodic Defected Ground Structures
Combinatorial Nonperiodic Defected ground structures (CNPDGS) are expanded from the photonics bandgap (PBG) structures. On the ground metallic plane, the defected units are etched artificially, the ground current distribution can be changed, and the frequency properties of the transmission lines can be influenced. In this paper, artificial neural networks (ANNs) of CNPDGS are developed. The structure sizes of CNPDGS and the frequency are defined as the input samples of the ANNs model, and the parameters of transmission coefficient are defined as the output samples. As the ANNs model has been trained with the Bayesian Regularization algorithm, the transmission coefficient of CNPDGS at any arbitrary sizes and the frequencies can be obtained quickly from the ANNs model. The result indicates that the way of ANNs model has the advantages of saving time and accuracy, and it is very useful in practice.
Combinatorial nonperiodic defected ground structures (CNPDGS) BP neural networks Bayesian Regularization algorithm Transmission coefficient
JIN Taobin DING Ronglin ZHANG Yongfang SUN Jiamin
School of Electronic Information Engineering, Tianjin University, Tianjin ,300072
国际会议
北京
英文
2007-08-05（万方平台首次上网日期，不代表论文的发表时间）