Application of a Radial Basis Function Neural Network for the Inverse Electromagnetic Problem of Parameter Identification

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network (FFNN) with one hidden layer, namely radial basis function (RBF) neural network and finite element method (FEM) to solve the electromagnetic inverse problem of parameter identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the FEM. Both variations in relative magnetic permeability and electrical conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of RBF neural network. Finally, the obtained neural network (NN) is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. Performance of the RBF network was also compared with the most commonly used multilayer perceptron (MLP) network model. The obtained results show that RBF network performs better than MLP network model.