A neural network based estimation method for magnetic shielding at extremely low frequencies

The attenuation of extremely low-frequency magnetic fields is important in reducing electromagnetic interference on electric and electronic equipment. In this paper, an innovative method is presented for shielded magnetic field level estimation at power frequencies by a neural network (NN) technique which uses experimental data. The utilized NN is applied to cylindrical shields (transformer-grade iron, copper, and aluminum) in various shield arrangements. Using the developed NN model, the mitigated magnetic field of multilayered shields is measured and evaluated to predict the magnetic field at any distance apart from the magnetic source. The NN, which is based on a feed-forward neural network (FNN), is trained with scaled conjugate gradient, gradient descent with momentum and adaptive learning back propagation, and Levenberg–Marquardt algorithms to compute the shielded magnetic field. Results have shown that the developed FNN trained with the Levenberg–Marquardt algorithm is better than the other training algorithms in predicting the shielded magnetic field value accurately even in the presence of various shield arrangements.