LEARNING METHOD FOR A NEURAL NETWORK

In a learning method for training a recurrent neural network having a number of inputs and a number of outputs with at least one output being connected via a return line to an input, the return line is separated during training of the neural network, thereby freeing the input connected to the return line for use as an additional input during training, together with the other inputs. The additional input values, which must be estimated or predicted for supply to the thus-produced additional training inputs, are generated by treating each additional input value to be generated as a missing value in the time series of input quantities. Error distribution densities for the additional input values are calculated on the basis of the known values from the time series and their known or predetermined error distribution density, and samples are taken from this error distribution density according to the Monte Carlo method. These each lead to an estimated or predicted value whose average is introduced for the additional input value to be predicted. The method can be employed for the operation as well as for the training of the neural network, and is suitable for use in all known fields of utilization of neural networks.