Method and arrangement for the neural modelling of a dynamic system with non-linear stochastic behavior

A method for neural modelling of a dynamic system with non-linear stochastic behavior, the method comprising the steps of:

• modelling a system behavior as a time series of at least one influencing variable for a prediction;
• forming the influencing variable as an additive combination of a deterministic output quantity of a computerized recurrent neural network and a linearly modelled system error;
• and
• adapting a model of the system error with a neural model adaption error formed as a difference between the influencing variable supplied to the network at a point in time as an input quantity and a corresponding output quantity output by the adapted network.

A system for neural modelling of a dynamic environment with non-linear stochastic behavior, comprising:

• a computerized recurrent neural network for predicting at least one influencing variable of the dynamic environment;
• a first subsystem for error modelling of a system error of the dynamic environment;
• a second subsystem for forming an adaption error for a first point in time as a difference from the system error and the influencing variable at the first point in time;
• and
• a third subsystem for adapting the computerized recurrent neural network with the adaption error, whereby the computerized neural network is trained with a system behavior of said dynamic system.