Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A model calculation unit for calculating an RBF model is described, including a hard-wired processor core designed as hardware for calculating a fixedly predefined processing algorithm in coupled functional blocks, the processor core being designed to calculate an output variable for an RBF model as a function of one or multiple input variable(s) of nodes V[j,k], of length scales (L[j,k]), of weighting parameters p3[j,k] predefined for each node, the output variable being formed as a sum of a value calculated for each node V[j,k], the value resulting from a product of a weighting parameter p3[j,k] assigned to the particular node V[j,k], and a result of an exponential function of a value resulting from the input variable vector as a function of a square distance of the particular node (V[j,k]), weighted by the length scales (L[j,k]), the length scales (L[j,k]) being provided separately for each of the nodes as local length scales.

Abstract: A method for manipulation detection of a technical device, i.e., an exhaust gas after treatment device in a motor vehicle, including: providing an input vector including system variable(s) and including at least one control variable for an intervention in the technical device for successive time steps; using a data-based manipulation detection model to generate a corresponding output vector as a classification vector in each time step for each input vector, each output vector indicates a classification of a monitored variable in value ranges, for the input vector; providing an actual monitored variable based on at least one measured value in the successive time steps; creating a measurement classification vector from the actual monitored variable for each time step; detecting a manipulation as a function of the measurement classification vector and a first and a second comparison vector for time step(s) of the time window.

Abstract: A method for creating a model of a technical system as a function of measured sensor data of the technical system. The method includes the following steps: initializing a symbolic regression problem. A list of mathematical functions is established, including at least one linear and/or non-linear function and/or at least a one-dimensional parameterizable characteristic curve. The at least one-dimensional characteristic curve is implemented by a Smoothed Grid Regression (SGR) model. Solving the symbolic regression problem with the aid of a genetic algorithm.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Abstract: A device and a computer-implemented method for classifying data, in particular for a Controller Area Network or an automotive Ethernet network. A plurality of messages is received from a communications network. A message that has a predefined message type is selected for an input variable for an input model of a plurality of input models of an artificial neural network associated with the predefined message type. The input variable is determined as a function of the message, and in an output area of the artificial neural network a prediction is output that is usable for classifying the message as a function of the input variable, or a reconstruction of an input variable is output that is usable for classifying the message as a function of this input variable.

Abstract: A method for ascertaining an anomaly in a communications network. In a first phase, a discriminator is trained to recognize whether messages transmitted over the communications network are indicative of the anomaly existing; during training, normal data and artificial data produced by a generator are fed to the discriminator, and, in response, the discriminator is trained to recognize that normal data being fed thereto connotes no anomaly, and artificial data being fed thereto connotes an anomaly. In a second phase, the generator is trained to produce artificial data which, when fed to the discriminator, are classified with the greatest possible probability as normal data. In a third phase, contents of messages received over the communications network are fed as an input variable to the discriminator; an output variable is ascertained, and the decision as to whether the anomaly exists or not being made as a function of the output variable.

Abstract: A model calculation unit for calculating an RBF model is described, including a hard-wired processor core designed as hardware for calculating a fixedly predefined processing algorithm in coupled functional blocks, the processor core being designed to calculate an output variable for an RBF model as a function of one or multiple input variable(s) of nodes V[j,k], of length scales (L[j,k]), of weighting parameters p3[j,k] predefined for each node, the output variable being formed as a sum of a value calculated for each node V[j,k], the value resulting from a product of a weighting parameter p3[j,k] assigned to the particular node V[j,k], and a result of an exponential function of a value resulting from the input variable vector as a function of a square distance of the particular node (V[j,k]), weighted by the length scales (L[j,k]), the length scales (L[j,k]) being provided separately for each of the nodes as local length scales.

Abstract: A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Abstract: A method is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.

Abstract: A method is provided for populating a function for a control unit with data, in which method measurements are performed on a system at different measuring points on a test stand, and a global data-based model is set up based on the obtained measured values, and virtual measurements which simulate real measurements on the test stand are carried out on the global data-based model, and uncertainties for virtual measured values of the virtual measurements are determined from the global data-based model, the uncertainties of the virtual measured values being taken into account when populating the function for the control unit with data.

Abstract: A microcontroller having a computing unit and a logic circuit. The microcontroller carries out computations for a regulation or control in a vehicle. The computing unit is connected to the logic circuit, and the logic circuit has an arrangement for computing an exponential function and is configurable.

Abstract: A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Abstract: A method for controlling an internal combustion engine includes: providing a setpoint value of at least one combustion attribute on the basis of a setpoint value characteristics map; determining from a control variable characteristics map a value of a characteristics-map-based control variable for controlling the engine; ascertaining with the aid of a data-based model a value of a modified control variable for controlling the engine, the data-based model specifying a predicted combustion attribute as a function of a real value of the combustion attribute of the preceding combustion, and the value of the modified control variable for controlling the engine being ascertained from the predicted combustion attribute; and providing a real control variable set to a value that is a function of the value of the characteristics-map-based control variable and/or the value of the modified control variable.

Abstract: A microcontroller having a computing unit and a logic circuit. The microcontroller carries out computations for a regulation or control in a vehicle. The computing unit is connected to the logic circuit, and the logic circuit has an arrangement for computing an exponential function and is configurable.

Abstract: A method for controlling an internal combustion engine includes: providing a setpoint value of at least one combustion attribute on the basis of a setpoint value characteristics map; determining from a control variable characteristics map a value of a characteristics-map-based control variable for controlling the engine; ascertaining with the aid of a data-based model a value of a modified control variable for controlling the engine, the data-based model specifying a predicted combustion attribute as a function of a real value of the combustion attribute of the preceding combustion, and the value of the modified control variable for controlling the engine being ascertained from the predicted combustion attribute; and providing a real control variable set to a value that is a function of the value of the characteristics-map-based control variable and/or the value of the modified control variable.