Regularised training of neural networks

Abstract

Training an artificial neural network, ANN, which translates one or more input variables into one or more output variables, using learning data sets including learning input variable values having measurement data, and associated learning output variable values, by: mapping learning input variable values from a learning data set onto output variable values using the ANN; processing deviations of the output variable values from the respective learning output variable values using a cost function to form a measure of the error of the ANN when processing the learning input variable values; determining from the error, by backpropagation, changes in parameters, the execution of which, when learning input variable values are further processed by the ANN, improve the evaluation of the obtained output variable values by the cost function, and applying said changes to the ANN; wherein a subset of the output variable values is excluded from consideration in the backpropagation.