volume | PIER Journals

Abstract

This paper examines the optimisation of antenna parameters for wire monopole, vertical trapezoidal monopole, and circular disc monopole antennas with the Enhanced Optimizable Neural Network Classifier (EONNC) and Sugeno Fuzzy Inference System (SFIS). This study includes both quantitative and conventional antenna design techniques, offering comprehensive insights into antenna optimisation tactics. An advanced antenna selection algorithm identifies the ideal antenna by a comprehensive examination of performance metrics with the EONNC, hence reinforcing the rigour of our research process. The geometric parameters of the antennas are delineated, with SFIS proficiently ascertaining the appropriate dimensions. The EONNC categorises antennas into three classifications, whereas the SFIS determines optimal parameters for estimating antenna size. Accuracy measures assess the EONNC performance, whereas the SFIS performance is measured using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our suggested technique demonstrates remarkable precision in parameter prediction and antenna classification, with a mean absolute percentage error (MAPE) of less than 4% and an accuracy exceeding 99.3%. The research examines the circular disc monopole antenna due to limitations in simulation duration for SAR measurements, resulting in SAR values of 0.978 W/kg for arm measurements and 0.985 W/kg for hand measurements. The proposed techniques are very relevant to actual antenna designs, especially for wearable applications.

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Mr. Rajendran Ramasamy

Dr. Samidoss Chinnapparaj

Dr. Vellaichamy Rajavel

Dr. Venkatesh Pandi Ravichandran

Dr. Abbas Ali Farithkhan

Dr. Amanulla Yasmin Jenifer