Fast and accurate modelling of frequencyselective surfaces using a new modular neural network configuration of multilayer perceptrons

The authors consider the analysis and modelling of the scattering from frequency-selective surfaces (FSSs), in the 6-14-GHz band, as a function of its periodic array geometry of thin dipole elements on an anisotropic layer. The accurate full-wave electromagnetic (EM) analysis of each FSS was carried out using the method of moments. From the available EM data, the artificial neural network (ANN) models can be developed. The modelling problem was solved by using a new modular configuration of multilayer perceptrons (MLPs), which is an implementation of the proposal modified from the previous knowledge method of neuromodelling information. Each MLP in the modular configuration was trained separately from the others through the resilient backpropagation algorithm. Within the region of interest studied, the ANN model developed is able to estimate the resonance frequencies and the bandwidths of the FSS band-stop filters, with high accuracy and low computational cost. To verify the advantageous properties of the modular MLP/MLP model, a neural model using a simple MLP was developed in order to analyse the same learning task. A comparative study was done between these models in terms of training the convergence, the accuracy and the computational cost.