Abstract: A computer-implemented method for assessing at least one printed circuit board includes receiving input data based on testing data of a printed circuit board, wherein the testing data represent in-circuit test testing data and include measurement data of a plurality of electronic components of the printed circuit board, applying a trained classification function to the input data, and generating and providing output data. The output data include an assignment of at least one of the electronic components to one of at least two different classes.

Abstract: A computer-Implemented method, system, and computer program product for optimizing production of an industrial facility. The industrial facility is designed to produce a predefinable quantity of at least one product. A model trained by machine learning is provided at a first time and the trained model is executed at a second time following the first time to generate a rolling forecast for a predefinable time interval. The predefinable time interval begins after the second time and the rolling forecast forecasts for any time within the time interval a quantity of the at least one product to be produced at this time. The rolling forecast is further processed by means of a further model to calculate a reforecast on the basis of the rolling forecast.

Abstract: A method for transforming a fuzzy logic system into a neural network, where, in order to simulate membership functions, sigmoid functions are linked together in such a way that, even after the optimization of the neural network, back-transformation of the neural network into a, fuzzy logic system is possible. The advantage of the method described is that a fuzzy logic system can be transformed, in particular component by component, into a neural network and the latter can then be optimized as a whole, i.e. all the components together. The possibility of back-transforming the trained neural network ultimately means that an optimized fuzzy logic system can be obtained. This advantageously makes it possible to use, in particular, standardized fuzzy system software for describing the optimized fuzzy logic system.

Abstract: A method for transformation of fuzzy logic (FS) into a neural network (NN), in which, in order to form a defuzzified output value (y2) from normalized single-element functions (F1 . . . Fm), the single-element functions (F1 . . . Fm) are each assigned a singleton position (A1 . . . Am) and at least one singleton weighting factor (R1 . . . Rn), those singleton weighting factors (R1 . . . Rn) which are assigned to the same single-element function (F1 . . . Fm) are additively linked, and the singleton weighting factors (R1 . . . Rn) and the additively linked singleton weighting factors (R1 . . . Rn) are weighted via the corresponding singleton positions (A1 . . . Am) and are additively linked in order to form the defuzzified output value (y2). One advantage of the method according to the invention is that the singleton positions (A1 . . .