A Structure Parameter Estimation Method for Microstrip BPF Based on Multilayer FCN

Abstract

A novel structure parameter estimation method for microstrip bandpass filter (BPF) based on multilayer fully connected network (FCN) is proposed in this letter. We design a network called the main-sub (MS)-Net, which includes the main-network to estimate the structure parameters and the subnetwork to predict the frequency responses of the BPFs. Compared with other neural network-based optimization methods, the MS-Net can generate its own data during the learning process without the need of collecting data sets and pretraining the network. The structure parameters estimated by Main-Net will gradually satisfy the design specifications in the directly iterative learning process. To demonstrate the validity of the proposed method, it was used for designing a third-order and a fifth-order microstrip BPFs. The experiment results show that the proposed method is valid and effective.