Inverse Modeling for Filters Using a Regularized Deep Neural Network Approach

Abstract

Extracting coupling matrix from given S-parameters can be viewed as an inverse problem for microwave filters, which is of importance for filter design and tuning. In this letter, a regularized deep belief network (R-DBN) is proposed to handle this inverse modeling problem. The training of an R-DBN consists of two steps. First, in unsupervised training, to accommodate the characteristics of input data, this model is constructed with a series of traditional restricted Boltzmann machines (RBMs), which are equipped with a continuous version of transfer function for continuous data processing. In addition, this training can provide suitable weights and bias for the following step. Second, in supervised training, Bayesian regularization is employed to increase modeling ability and prevent overfitting. Two experiments with different simulation environments are illustrated, and the calibration results show high accuracy and robustness in a more intelligent way using this method.