Further training of neural networks for the evaluation of measurement data

Abstract

A method for further training of a neural network for processing measurement data, which neural network has been pre-trained with training examples from a set M. In the method: a batch B of new training examples is provided; a subset D⊆M of the previous training examples is provided; the new training examples from batch B and the previous training examples from subset D are processed by the neural network into outputs respectively; the deviations of the outputs from the respective target outputs are evaluated using a predefined cost function; parameters characterizing the behavior of the neural network are optimized with the aim that, during further processing of previous and new training examples, the evaluation with the cost function is improved in regard to new training examples from batch B and is not made worse in regard to previous training examples from subset D.