An Efficient Antenna Parameters Estimation Using Machine Learning Algorithms

Abstract

A smart antenna synthesis approach is described as automatically choosing the optimum antenna type and providing the best geometric characteristics under the demands of antenna performance. Different antenna performance characteristics are examined, and using decision tree classifier, the optimal antenna is suggested using an intelligent antenna selection model. Finally, the geometric characteristics of the antenna are given before the fuzzy inference system is developed by merging five primary learners to fully exploit the benefits of each type of learner. Rectangular patch antenna, pyramidal horn antenna, and helical antenna are the three types of antennas that are classified by a decision tree classifier, and the optimal antenna size parameters are determined using a fuzzy inference method. The performance of decision tree classifier measured using accuracy and FIS is measured using Mean Square Error (MSE) and MAPE. The system demonstrates excellent capability in parameter prediction with antenna categorization with a MAPE of less than 5.8% and accuracy over 99% achieved in our proposed method. The recommended methodology might be widely applied in actual smart antenna design.