Modeling of the cylindrical metallic cavity with circular cross-section using neural networks

In this paper, a loaded cylindrical metallic cavity with circular cross-section is modeled using classical multi-layer perception (MLP) network. The load in the form of a homogeneous dielectric slab with losses located on the bottom of the cavity is considered. Several training approaches of the neural model are applied, where an original approach for decreasing the neural network training set is used. The obtained results are discussed and compared. Also, the modeling results are experimentally verified.