Artificial Neural Network for Dimensionality Reduction and Its Application to Microwave Filters Inverse Modeling

Abstract

The purpose of filters’ inverse modeling is to acquire the values of physical or geometrical parameters for the specified electrical response. In this article, a dimensionality reduction (DR) strategy is proposed through the artificial neural network (ANN) approach, for the first time, to simplify the filters’ inverse modeling. First, all physical/geometrical variables are divided into two parts, partial variables and other ones, through a selection mechanism, and only partial variables are to be determined in the inverse modeling solution. Then, an ANN for DR (DR-ANN) is constructed to form the relationship between partial variables and other ones for given electrical response. Third, the predicted partial variables are set as known input and then run the DR-ANN to acquire the value of remaining variables. The proposed DR-ANN framework can be easily introduced to many existing inverse modeling problems. Specifically, a multivalued neural network approach and a well-known differential evolutional (DE) optimization method are combined with DR-ANN. The improvement of the modified methods is demonstrated with some examples, including coupling matrix extraction and waveguide filter optimization. The experiment results show that the proposed solution is more robust with faster convergence in comparison with existing methods.