To develop a neural model of a dynamic system with a nonlinear stoichiometric behavior, the system behavior is modeled for a forecast as a time sequence of at least one influence value of the system. The influence value is formed as an additive combination from a deterministic start value (y asterisk ) of a recurrent neural network (NN) and a linear modeled system fault ( epsilon ). At an initial time point, the recurrent neural network is adapted with the fault model adaptation fault, as a difference between the measured influence value at the first time point and the system fault. In an Independent claim an assembly identifies a recurrent neural network (NN) for the prognosis of at least one influence value on the system. It can produce a fault model (L) of a system fault ( epsilon ) of the dynamic system. It can show an adaptation fault for an initial time point. It can adapt the recurrent neural network with the adaptation fault, at least by training the neural network with the system behavior. Preferred Features: The system fault ( epsilon ) is modeled with a Kalman filter, where a missing measurement value in the time sequence is modeled infinitely with a variance, and a measurement value in the time sequence is modeled with a zero variance. The operation uses the equations: and the measurement value equation: where epsilon t and delta t are the additive noises with Yt asterisk the response, Yt the influence value and Xt the system fault. The recurrent neural network (NN) is trained with the real time learning protocol for neural networks. The linear fault model (L) is adapted with forwards/backwards Kalman filter equations. The neural network (NN) is a multi-layer Perceptron, with a hidden layer of three neurons. The measured blood sugar value can be inserted.
Method and device for the neuronal modelling of a dynamic system with non-linear stochastic behavior