Metamaterial Parameter Estimation by Machine Learning Method

Abstract Artificial neural network modeling is used to synthesize the metamaterial unit cell. Artificial neural networks are powerful tools to establish the relation between inputs and outputs parameters under highly nonlinear conditions. Artificial neural networks captured the synaptic weights according to their training data set. In artificial neural networks, the back propagation technique is the fastest learning method, which reduces the computer’s processing time and provides the best results under the nonlinear relationship between input and output. This work is divided into three parts. In the first part, we design a metamaterial unit cell, which is in the shape of square split rings. This shape is widely used to realize a metamaterial unit cell. In the second part, we develop a regression model using artificial neural networks to estimate the output resonance frequency when design parameters are used as input of artificial neural networks. In the last part, we use three different machine learning method to estimate the output parameter and then do the comparison in between them. Therefore, the objective of this research work is to develop a hypothesis using feed forward backpropagation method, Bayesian regularization and Elman backpropagation method, to find the resonance frequency when dimension of the metamaterial unit cell is given.