Parametric Modeling of EM Behavior of Microwave Components Using Combined Neural Networks and Pole-Residue-Based Transfer Functions

This paper proposes an advanced technique to develop combined neural network and pole-residue-based transfer function models for parametric modeling of electromagnetic (EM) behavior of microwave components. In this technique, neural networks are trained to learn the relationship between pole/residues of the transfer functions and geometrical parameters. The order of the pole-residue transfer function may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed modeling process, the trained model can be used to provide accurate and fast prediction of the EM behavior of microwave components with geometrical parameters as variables. The proposed method can obtain better accuracy in challenging applications involving high dimension of geometrical parameter space and large geometrical variations, compared with conventional modeling methods. The proposed technique is effective and robust especially in solving high-order problems. This technique is illustrated by three examples of EM parametric modeling.