Abstract
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.
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Wang, BZ. Artificial Neural Network Models for Coaxial to Waveguide Adapters. International Journal of Infrared and Millimeter Waves 20, 125–136 (1999). https://doi.org/10.1023/A:1021711903516
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DOI: https://doi.org/10.1023/A:1021711903516