Abstract: A process model of an industrial process or system is generated. The model correlates a first number M of process parameters forming input values with a second number L of quality characteristics forming output values, which are processed to form feedback control signals for the process or system. A third number N of training data sets of the industrial process are first gathered and processed during a learning phase of the model with the help of a central processing unit, whereby a preliminary approximately model is used including a neural network with local approximation characteristics. The neural network is connected in parallel with a linear network. Both networks are connected to the same inputs. The neural network initially has a number N of neural cells corresponding to the number of training data sets. A weighted linear combination of the M process parameters is performed. The linear network and the neural network are connected with their outputs through weighting circuits to a common summing point.