Method for editing an input quantity for a neural network

A method for training a computerized neural network, comprising the steps of:

• a) in a computer, forming a time series of a set of measured values of a variable dynamic input quantity by determining respective values of the input quantity at discrete points in time;
• b) in said computer, identifying a statistical noise distribution of an uncorrelated noise of finite variance that has a chronological average of zero and is superimposed on the measured values;
• c) in said computer, generating a substitute value for any missing measured values in the time series by, for each missing value, calculating a statistical missing value noise distribution according to said known noise distribution from at least one of said measured values neighboring the missing value in the time series and calculating said substitute value from a plurality of Monte Carlo samples of the missing value obtained according to the missing value noise distribution and replacing said missing value with said substitute value;
• d) supplying said time series, with the substitute value generated in step (c) replacing any missing value, from said computer to a computerized neural network as input quantities;
• and
• e) training said neural network using said time series and a behavior of a technical system represented by the neural network.