Abstract
In this paper, the loaded cylindrical metallic cavity is modeled using a new hhybrid empirical-neural (HEN) model. The considered load is a homogeneous dielectric layer, which is elevated from the cavity bottom. Unlike the model based on classical multi-layer perceptron (MLP) network, the proposed HEN model includes an existing partial knowledge about the resonant frequency behavior of the cavity, yielding more accurate determination of the resonant frequencies. Comparison of MLP and HEN models as well as an advantage of using the HEN model is given through an example referring to the experimental cylindrical metallic cavity with a circular cross-section
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