A Novel Application of MLP Networks in Classifying L Band Eco-Friendly Microwave Absorbers

This paper presents an innovative approach to classifying the absorption performance of eco-friendly microwave absorbers in the L band using Multilayer Perceptron (MLP) networks. This project uses pyramidal absorbers coated with agricultural waste materials, such as empty palm oil bunches and coconut shells as carbon material to improve their absorption properties. The dataset consists of 87 absorption performance values of microwave absorbers obtained from experimental measurements using the NRL Arch Free. The objective of this study is to compare the effectiveness of three training algorithms which are Levenberg-Marquardt (LM), Resilient Backpropagation (RB) and Scale-Conjugate Gradient (SCG). The MLP network was trained using input parameters of frequency and absorption performance, and the performance of each algorithm was evaluated based on accuracy and mean-squared error (MSE). Results show that the LM algorithm with five hidden neurons achieved the highest training, validation and testing accuracy of 100% with the lowest MSE of 0.0455. These findings provide valuable insights for optimizing the design of microwave absorbers in the L band using eco-friendly materials.