Robert Boschgmbh

Holger Ulmer

Heiner Markert

Ernst Kloppenburg

Felix Streichert

Andrej Junginger

Thomas Kruse

Axel Aue

Tobias Lang

Nico Bannow

Volker Imhof

Thilo Strauss

Ulrich Schulmeister

Markus Hanselmann

Rene Diener

Michael Saetzler

Jens Stefan Buchner

Thomas Richardsen

Sebastian Boblest

Udo Schulz

Andre Guntoro

Axel Loeffler

Matthias Schreiber

Stefan Angermaier

Katharina Dormann

Thorsten Huber

Patrick Engel

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.

Method and device for creating a model of a technical system from measurements

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.

Device and method for classifying data in particular for a controller area network or an automotive ethernet network

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.

Model calculation unit and control unit for calculating an RBF model

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.

Method and device for ascertaining anomalies in a communications network

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.

Control device and method for calculating an output parameter for a controller

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.

Method for setting up a functionality for a control unit

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.

Microcontroller having a computing unit and a logic circuit, and method for carrying out computations by a microcontroller for a regulation or a control in a vehicle

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.

Method and engine control unit for controlling an internal combustion engine

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Ulm, Germany

Holger Ulmer