An efficient modeling technique for RF MEMS phase shifter based on RBF neural network

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 three-dimensional 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 within 1 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.