This paper presents an analysis of use of artificial neural network algorithm for prediction of power loss and relative permeability in toroidal cores wound from grain-oriented electrical steel sheet and cobalt-based amorphous ribbon. The properties of the grain-oriented samples were measured at peak flux densities from 0.3 to 1.8T and frequencies from 20Hz to 1kHz, and those of the cobalt-based samples were measured at peak flux densities from 0.1 to 0.5T and over a frequency range from 20Hz to 25kHz. Measurements were carried out under sinusoidal flux density and pulse-width-modulated voltage supplies. In each case, 80% of the measured results were used for the training procedure and 20% for detection of over-training. It has been found that optimisation of training data significantly increases the accuracy of power loss prediction. The prediction errors of the range of measured results of power loss and permeability for the grain-oriented cores are lower than ±3% with 97% confidence level and ±4% with 83%, respectively. For the cobalt-amorphous cores, these values are ±10% with 95% confidence and ±10% with 85% confidence, respectively.
