Loaded cylindrical metallic cavities modeling using neural networks

The feasibility of using neural networks for resonant frequencies determination in loaded cylindrical metallic cavities is presented. The load in the form of a homogeneous dielectric slab with losses, which is located on the bottom of the cavity, is considered. The use of the classical multilayer perceptron (MLP) network is illustrated through the example of TM/sub 113/ modes determination in a microwave cavity with circular cross-section. An original approach for decreasing the neural network training set is used.