Abstract
In general, if measurements can be repeated several times assuming the same conditions, the measurement error can significantly be decreased by statistically evaluating the measurements. However, an uncertainty band always remains. Non-linear numerical simulations based on e.g. the Newton-Raphson method may establish a poor convergence if they are provided directly with measured data. Therefore, data pre-processing is required. Here, a neural network approach is employed. A two-layer perceptron is fitted on a measured magnetisation curve, thereby restricting the solution to be technically feasible while accepting the statistical nature of the data. By using a perceptron, an analytical expression of the magnetisation curve is obtained and expressions for its derivatives can easily be computed.