An Efficient Modeling Technique for RF MEMS Phase Shifter Based on RBF Neural Network

摘要：

A modeling technique based on RBF neural network is presented for the design of RF MEMS phase shifter. Three sensitive parameters are selected according to complicated threedimensional structure design of an RF MEMS phase shifter and used as inputs of neural network. Experiments show that the proposed approach in this paper is a high efficiency modeling for the RF characteristics analysis for RF MEMS phase shifter. The training of the RBF neural network is accomplished within 30 minutes using 27*51 samples. The trained RBF neural network is able to predict the outputs for 51 test samples with in I minute.Comparison between RBF neural network predictions and HFSS simulations show that the root mean square relatively errors,mean absolute relatively errors and maximize absolute relatively errors are less than 0.0368,0.0417 and 0.0442 respectively.

作者: G.H.Yang Q.Wu J.H.Fu K.Tang J.X.He

作者单位: School of Electronics and Information Technology,Harbin Institute of Technology,China,150001 POBox 341,92,Xidazhi Street Harbin,Heilongjiang 150001 China

会议类型: 国际会议

会议名称: 2008 International Conference on Microwave and Millimeter Wave Technology(2008国际微波毫米波技术会议)

会议地点: 南京

会议语种:英文

页码: 475-478

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）