Abstract: A method for predicting a remaining time of a signal phase includes capturing traffic data and a signal phase specification distinguishing different signal phases of a traffic signal generator. The traffic data is fed as input data to an artificial neural network including first and second sub-networks and a combination network for combining output data of the two sub-networks. The artificial neural network is trained to reproduce a time still remaining until a phase change of the traffic signal generator based on the traffic data. Outputting of the output data of the first and second sub-networks is controlled in a manner complementary to one another according to the signal phase specification. Lastly, the output data of the combination network or the prediction data derived therefrom are transmitted to a transport device or to a road user as a prediction of the time remaining for influencing traffic.

Abstract: For controlling a production system product version-specific training data sets are read in for each of multiple product versions. Each training data set comprises a design data set. The design data sets are fed into a machine learning module covering all product versions. An output signal is fed into both a first product version-specific machine learning module and also a second product version-specific machine learning module. The machine learning modules are jointly trained so that output data (O1) of the first machine learning module reproduces the performance values of the first product version and output data of the second machine learning module reproduces the performance values of the second product version. Then, a plurality of synthetic design data sets are generated and fed into the trained machine learning module. The resulting output signal is fed into the trained first machine learning module. A performance-optimized design data set is derived.

Abstract: A machine learning module is provided trained to generate from a design data record specifying a design variant, a predictive performance distribution and a constraint compliance distribution of the design variant. A predictive performance distribution and a constraint compliance distribution are generated by the machine learning module. The predictive performance distribution is compared with performance values of previously evaluated design data records. A simulation of the corresponding design variant is either run or skipped. A design evaluation record is output which includes a performance value and constraint compliance data each derived from the simulation if the simulation is run or, otherwise, each derived from the predictive performance distribution and the constraint compliance distribution. Depending on the design evaluation records, a performance-optimizing and constraint-compliant design data record is selected from the variety of design data records.

Abstract: A plurality of test data sets include: a first design data set specifying a design variant of a product; and first target values, which quantify target variables of the design variant which are to be optimized and ranked. Furthermore, a plurality of design evaluation modules for predicting target values on the basis of design data sets is provided. For each of the design evaluation modules, a second ranking of the first design data sets with respect to the predicted target values and a deviation of the second ranking from the first ranking are then determined. One design evaluation module is then selected in accordance with the determined deviations. Furthermore, a plurality of second design data sets is generated, and are predicted by the selected design evaluation module. A target-value-optimized design data set is then derived from the second design data sets and is output for the manufacturing of the product.

Abstract: A computer-implemented method for determining at least one remaining time value, to be determined, for a system is provided, having the following steps: a. providing at least one known input data record containing a multiplicity of input elements for at least one determined time; b. providing at least one associated known remaining time value for the at least one input data record; c. determining the at least one remaining time value to be determined by applying an error function to the at least one input data record and the at least one associated remaining time value; and d. providing an output data record containing the at least one determined remaining time value and an associated reliability value. The invention furthermore targets a corresponding determination unit and computer program product.

Abstract: In order to reproduce noise components of lossy recorded operating signals of a technical system, a neural network is trained to reproduce a recorded target operating signal and a statistical distribution of a stochastic component of the recorded target operating signal on the basis of a recorded input operating signal. A current input operating signal of the technical system is then supplied to the trained neural network. An output signal having a noise component modelled on the statistical distribution is generated on the basis of the supplied current input operating signal and a noise signal. The output signal is then output as the current target operating signal for controlling the technical system.

Abstract: For a multiplicity of design variants of a technical product, a training structural data set specifying the particular design variant and a training quality value quantifying a predefined design criterion are read in in each case as training data. The training data are taken as a basis for training a Bayesian neural network to determine an associated quality value, together with an associated uncertainty comment, on the basis of a structural data set. Furthermore, a multiplicity of synthetic structural data sets are generated and fed into the trained Bayesian neural network which generates a quality value with an associated uncertainty comment for each of the synthetic structural data sets. The uncertainty comments generated are compared with a predefined reliability comment and one of the synthetic structural data sets is selected on the basis thereof. The selected structural data set is then output for the purpose of producing the technical product.

Abstract: A computer-implemented method for configuring a system model and a computer-implemented method for configuring a sensor model. There is also described a computer-implemented method for determining future switching behavior of a system unit, with the following steps: a) receiving the configured system model; b) receiving the configured sensor model, c) the configured sensor model being a probability distribution regarding how the sensor unit will behave in the specific time period; d) establishing at least one random sample of behavior of a sensor unit by sampling from the probability distribution; and e) determining the future switching behavior of the system unit and/or at least one associated statistical value on the basis of the established random sample by means of the trained system model. There is also described a corresponding computer program product.

Abstract: A method for predicting a remaining time of a signal phase includes capturing traffic data and a signal phase specification distinguishing different signal phases of a traffic signal generator. The traffic data is fed as input data to an artificial neural network including first and second sub-networks and a combination network for combining output data of the two sub-networks. The artificial neural network is trained to reproduce a time still remaining until a phase change of the traffic signal generator based on the traffic data. Outputting of the output data of the first and second sub-networks is controlled in a manner complementary to one another according to the signal phase specification. Lastly, the output data of the combination network or the prediction data derived therefrom are transmitted to a transport device or to a road user as a prediction of the time remaining for influencing traffic.

Abstract: A computer-implemented method for determining at least one remaining time value, to be determined, for a system is provided, having the following steps: a. providing at least one known input data record containing a multiplicity of input elements for at least one determined time; b. providing at least one associated known remaining time value for the at least one input data record; c. determining the at least one remaining time value to be determined by applying an error function to the at least one input data record and the at least one associated remaining time value; and d. providing an output data record containing the at least one determined remaining time value and an associated reliability value. The invention furthermore targets a corresponding determination unit and computer program product.

Abstract: A machine learning module is provided which is trained to generate from a design data record specifying a design variant of a product, a first performance signal quantifying a predictive performance of the design variant and a predictive uncertainty of the predictive performance. A variety of design data records each specifying a design variant of the product is generated. For a respective design data record, the following steps are performed: a first performance signal and a corresponding predictive uncertainty are generated, depending on the predictive uncertainty, a simulation yielding a second performance signal quantifying a simulated performance of the corresponding design variant is either run or skipped, and a performance value is derived from the second performance signal if the simulation is run or, otherwise, from the first performance signal. Depending on the derived performance values, a performance-optimizing design data record is determined and output to control the production plant.

Abstract: To train a machine learning routine (BNN), a sequence of first training data (PIC) is read in through the machine learning routine. The machine learning routine is trained using the first training data, wherein a plurality of learning parameters (LP) of the machine learning routine is set by the training. Furthermore, a value distribution (VLP) of the learning parameters, which occurs during the training, is determined and a continuation signal (CN) is generated on the basis of the determined value distribution of the learning parameters. Depending on the continuation signal, the training is then continued with a further sequence of the first training data or other training data (PIC2) are requested for the training.