Abstract: Method for controlling and preconfiguring a steelworks or parts of a steelworks, the rolling stand or the rolling mill train being controlled and preconfigured by means of a model of the rolling stand or the rolling mill train, the model having at least one neural network whose parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, the rate at which the parameters are matched or adapted to the actual conditions in the rolling stand or in the rolling mill train, in particular to the properties of the strip, being varied.

Abstract: The invention concerns a method and a computer programme for producing diagrammatic network plans. On the basis of an existing plan wherein connections are subdivided in line sections, and where line sections can be represented by edges, and network elements delimiting the line segments can be represented by nodes, and where mutually parallel line segments can be associated with profiles, data concerning a spatial arrangement of network elements to be modified are stored in a diagrammatic plan database. By moving away predetermined nodes, edges are assembled and profiles are smoothed. An arrangement of the edges associated with the profiles in the respective profiles is determined by means of an algorithm for competitive mode graphics, which is used on pairs of connections of lines having at least a parallel line segment, while taking into account a plurality of intersection points between the connections of the respective lines.

Abstract: A learning process for a neural network for open-loop or closed-loop control of an industrial process with time-variable parameters. The neural network is configured either as an open-loop or closed-loop-control network with which the process is controlled. The neural network is trained with the current process data so that it builds a model of the current process. The neural network can also be configured as a background network which is trained during operation with representative process data so that it builds an averaged model of the process over a longer period of time. After a certain learning time or upon the occurrence of an external event, the open-loop or closed-control network is replaced by the background network.