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: 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.

Abstract: A method for identifying or predicting process parameters of an industrial process, in particular a primary-industry plant, having especially quickly varying process parameters or disturbances affecting the process, with the process parameters to be identified being determined by a process model as a function of measured values from the process, and with the process model having at least one time-invariant or one largely time-invariant process model which represents an image of the process averaged over time, and at least one time-variant process model that is adjusted to at least one time constant of a disturbance or of a variation in parameters of the process.

Abstract: Optimum values for manipulated variables s.sub.1, . . . ,s.sub.S of a technical system (.phi.) are determined for prescribed operational variables b.sub.1, . . . ,b.sub.B in order to optimize a prescribed target function z=g(y.sub.2. . . ,y.sub.N) of system properties y.sub.2, . . . , y.sub.N where y.sub.i =.phi..sub.i (s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B). Here, a set of functions f.sub.i (w.sub.1, . . . ,w.sub.W,x.sub.1, . . . ,x.sub.S=B) is used whose parameters w.sub.1, . . . ,w.sub.W are set in such a way that the functions f.sub.i model the system in such a way that the functions f.sub.i approximate the system functions .phi.i as functions of their variables x.sub.1, . . . ,x.sub.S+B in terms of an interval of prescribed magnitude; the manipulated variables s.sub.1, . . . ,s.sub.S are determined by optimizing the function g(f.sub.1 (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.B), . . . , f.sub.N (w.sub.1, . . . ,w.sub.W,s.sub.1, . . . ,s.sub.S,b.sub.1, . . . ,b.sub.

Abstract: A method for classifying field patterns generated by electrophysiological activities occurring inside the body of a living subject includes the steps of measuring field patterns arising as a result of the electrophysiological activities outside the body of the subject using a multi-channel measuring apparatus, generating feature vectors corresponding to the measured field patterns, supplying the feature vectors to an adaptive classifier, and training the adaptive classifier with training field patterns which have been generated by a localizable surrogate model of the electrophysiological activity. The method includes the further step of generating a probability value for each field pattern at an output of the adaptive classifier which indicates the probability with which each field pattern can be generated by a selected localizable surrogate model.

Abstract: A system for recognizing individually spoken words includes an acousto-electric transducer with a controllable amplifier and a high-pass filter connected to a level calculation path for calculating the level of the output signal by detecting the signal envelope and converting the envelope signal to a 1-bit digital signal to be supplied to a microprocessor. A signal analysis path is also connected to the transducer and includes a low-pass filter followed by a 1-bit analog to digital converter and a digital processing unit which supplies data units to the microprocessor. Individually spoken words are recognized and actions taken accordingly.