Artificial Neural Network Application in Prediction of Concrete Embedded Antenna Performance

Artificial Neural Network (ANN) has been extensively applied to microwave device modeling, design and simulations. In the present paper, the prediction of concrete embedded antenna performance using ANN is presented. The ANN model takes antenna embedded depth and concrete dielectric constant as inputs and gives antenna radiation efficiency, gain and input impedance as outputs. The Particle Swarm Optimisation (PSO) is employed to search the global optimal weights and bias for ANN, then Bayesian Regularisation (BR) is used to train the ANN for overcoming the overfitting issue. It is found that the PSO computation iteration for optimal network weights and bias searching is less than gradient descent algorithm. A PSO-BR neural network (PSO-BRNN) and back-propagation neural network (BPNN) are trained to compute and predict the antenna performance. The PSO-BRNN performance is better than BPNN in terms of accuracy and generalisation