The Back Propagation Neural Network Model of Non-Periodic Defected Ground Structure

摘要：

Presently,electromagnetic field numerical value analysis methods such as finite difference time-domain (FDTD)method are generally used to calculate the DGS,although these methods are accurate,they are also computationally expensive. In this paper,a neural network model of a novel defected ground structure is established. Since the neural network model has the advantages of great precision and effectiveness,the developed design model can be used to take the place of the FDTD method of the DGS,being a kind of aid tool of circuit design. The neural network models of two different non-periodic DGS have been developed,at the same time the circuit of the according DGS is designed and manufactured. The result of computer simulation and product measurements are obtained to demonstrate the effectiveness of the method.

关键词： neural network non-periodic DGS BP algorithm

作者: LI Yuan LIU Jiao YE Chunhui

作者单位: School of Electronic Information Engineering Tianjin University Tianjin 300072,China

会议类型: 国际会议

会议名称: 2008 Global Symposium on Millimeter Waves(GSMM 2008)(2008全球毫米波学术大会)

会议地点: 南京

会议语种:英文

页码: 29-32

在线出版日期: 2008-04-21（万方平台首次上网日期，不代表论文的发表时间）