{'fraction_non_alphanumeric': 0.015471167369901548, 'fraction_numerical': 0.0, 'mean_word_length': 5.780952380952381, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Optimization and Characterization of Electromagnetically Coupled Patch Antennas using RBF Neural Networks

A new neural network model is presented in this paper. It utilizes radial basis functions neural network. The model solves the problem of the electromagnetically coupled microstrip patch antennas. At a specific resonance frequency, the proposed model predicts the optimum geometrical dimensions of both the patch and feeding microstrip line. Moreover, it provides the important characteristics of the optimum design. These characteristics include the impedance bandwidth, gain, and radiation efficiency. The proposed neural network model is very accurate and extremely faster than the classical approach.