Artificial Neural Network Models for Coaxial to Waveguide Adapters

Artificial neural networks provide fast and accurate models for the modeling, simulation, and optimization of microwave and millimeter wave components. In this paper, a multilayer perceptron neural network (MLPNN) is used to model a millimeter wave coaxial to waveguide adapter. The MLPNN is electromagnetically developed with a set of training data that are produced by the full-wave finite-difference time-domain (FDTD) method. One type of the designs of experiments, the central composite technique, is used to allow for a minimum number of FDTD simulations that is needed to be performed. The MLPNN models are useful for the CAD of wideband coaxial to waveguide adapter.