Abstract: An operating signal is fed to a first machine learning module to reproduce a behavior signal of a technical system, the behavior signal occurring specifically without the current use of a control action and output the reproduced behavior signal as a first output signal. The first output signal is fed to a second machine learning module to reproduce a resulting behavior signal using a control action signal and output the reproduced behavior signal as a second output signal. Furthermore, an operating signal is fed to a third machine learning module, and a third output signal is fed to the trained second machine learning module. A control action performance ascertained using the second output signal, and the control action performance is used to train the third machine learning module to optimize the control action performance. By training the third machine learning module, a control device controls the technical system.

Abstract: A computer-implemented method, a device for data processing and a computer system for controlling a control device of a conveyor system to achieve an alignment and/or a defined spacing of piece goods, wherein the control of the control device is determined by an agent acting according to Reinforcement Learning methods. An individual, local state vector of predefined dimension that is the same for all the piece goods is created for each of the piece goods and an action vector is selected from an action space according to a strategy that is the same for all piece goods for the current state vector of this piece good. These action vectors are projected onto the conveying elements, wherein conflicts are resolved. After a cycle time has elapsed, state vectors are created again for each piece good and evaluated with rewards and the strategy is adjusted.

Abstract: A computer-implemented method for reducing friction within a machine tool is provided, including: a) reading a plurality of surrogate models for approximating friction compensation within a given machine tool, b) reading a friction compensation parameter set, c) determining a friction compensation result value for each surrogate model using the compensation parameter set, d) determining a weighted average friction compensation value of the friction compensation result values using the respective weighting factor, e) deducing a quality indicator for the friction compensation parameter set based on the weighted average friction compensation value, f) outputting the friction compensation parameter set, if the quality indicator fulfils a given quality criterion, or repeating b) to e) until the quality indicator fulfills the given quality criterion, g) applying the outputted friction compensation parameter set to the machine tool for reducing friction within the machine tool.

Abstract: A controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window-specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.

Abstract: To configure a machine controller by an action execution tree, predefined action patterns are read in. A multiplicity of action execution trees for a machine to be controlled is also generated. For a respectively generated action execution tree, a performance for controlling the machine based on the respective action execution tree is determined. The predefined action patterns are also sought in the respective action execution tree. An action pattern found in the respective action execution tree is then replaced at least in part by a reference to the predefined action pattern. A tree size of the thus modified action execution tree is furthermore determined. Based on the generated action execution trees, a numerical optimization method is then used to determine an action execution tree that is optimized with regard to better performance and smaller tree size, and this is output in order to configure the machine controller.

Abstract: Method and device for controlling a machine in accordance with to multiple control objectives in which machine control is based on automated learning of subordinate control skills, wherein the device provides multiple subordinate control skills which are each assigned to a different one of the multiple control objectives, the device provides multiple learning processes that are reinforcement learning processes that are each assigned to a different one of the multiple control objectives and are configured to optimize the corresponding subordinate control skill based on input data received from the machine, and where the device is configured to determine a superordinate control skill based on the subordinate control skills and to control the machine based on the superordinate control skill.

Abstract: An operating signal is fed to a first machine learning module to reproduce a behavior signal of a technical system, the behavior signal occurring specifically without the current use of a control action and output the reproduced behavior signal as a first output signal. The first output signal is fed to a second machine learning module to reproduce a resulting behavior signal using a control action signal and output the reproduced behavior signal as a second output signal. Furthermore, an operating signal is fed to a third machine learning module, and a third output signal is fed to the trained second machine learning module. A control action performance ascertained using the second output signal, and the control action performance is used to train the third machine learning module to optimize the control action performance. By training the third machine learning module, a control device controls the technical system.

Abstract: A computer-implemented method, a device for data processing and a computer system for controlling a control device of a conveyor system to achieve an alignment and/or a defined spacing of piece goods, wherein the control of the control device is determined by an agent acting according to Reinforcement Learning methods. An individual, local state vector of predefined dimension that is the same for all the piece goods is created for each of the piece goods and an action vector is selected from an action space according to a strategy that is the same for all piece goods for the current state vector of this piece good. These action vectors are projected onto the conveying elements, wherein conflicts are resolved. After a cycle time has elapsed, state vectors are created again for each piece good and evaluated with rewards and the strategy is adjusted.

Abstract: A method for teaching an electronic computing device includes at least a machine learning algorithm for predicting a position-based propagation of radio waves in an environment, including the steps of: providing a mathematical model for the position-based propagation, wherein the mathematical model includes at least a physical model for the position-based propagation in the environment generating training data for the machine learning algorithm including a propagation field and/or a propagation domain; training the machine learning algorithm by fitting the training data to a partial derivative of the machine learning algorithm; and obtaining a prediction of a propagation loss by a weighted sum of multiple evaluations of the trained machine learning algorithm. Furthermore, provided is a computer program product, a computer-readable storage medium as well as an electronic computing device.

Abstract: In order to configure a control device, a predefined default configuration data set is read in. Furthermore, a deviation from the default configuration data set as well as a control performance are determined for each of a large number of generated test configuration data sets. In addition, a Pareto optimization is performed for the large number of test configuration data sets, wherein the deviation as well as the control performance are used as Pareto objective criteria. A configuration data set resulting from the Pareto optimization is then selected to configure the control device.

Abstract: Provided is a method for the computer-assisted control of a technical system, in particular in a plant for generating energy, to achieve a predetermined technical behavior of the technical system, wherein an operating data set for controlling the system is provided. A system model for describing the mode of operation of the technical system is provided, wherein on the basis of the operating data set and on the basis of the system model, an optimization data set is determined by an optimization method. Based on the optimization data set, relevant parameters of the technical system that allow a more advantageous control of the technical system than other parameters of the technical system are selected using a selection method, wherein with the selected relevant parameters, a control method for the technical system is determined, wherein the technical system is controlled with the aid of the control method.

Abstract: A computer-implemented method for reducing friction within a machine tool is provided, including: a) reading a plurality of surrogate models for approximating friction compensation within a given machine tool, b) reading a friction compensation parameter set, c) determining a friction compensation result value for each surrogate model using the compensation parameter set, d) determining a weighted average friction compensation value of the friction compensation result values using the respective weighting factor, e) deducing a quality indicator for the friction compensation parameter set based on the weighted average friction compensation value, f) outputting the friction compensation parameter set, if the quality indicator fulfils a given quality criterion, or repeating b) to e) until the quality indicator fulfills the given quality criterion, g) applying the outputted friction compensation parameter set to the machine tool for reducing friction within the machine tool.

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 controller for a technical system is trained using a machine learning method. For this purpose, a chronological sequence of training data is detected for the machine learning method, the training data including both state data as well as control action data of the technical system. A change in the control action data over time is detected specifically and correlated with changes in the state data over time within different time windows, wherein a time window specific correlation value is ascertained in each case. A resulting time window is then ascertained on the basis of the ascertained correlation values, and the training data which is found within the resulting time window is extracted in a time window-specific manner. The controller is trained by means of the machine learning method using the extracted training data and thereby configured to control the technical system.

Abstract: A method for configuration of a controlled drive application of a logistics system. The logistics system includes parallel conveying paths for piece goods. Each conveying path includes sub-conveying paths which are each accelerated or delayed to merge the piece goods on a single output conveying path with defined spacing. A system model of the logistics system is firstly determined by operating data of the logistics system which include sensor values of the logistics system and changes to control variables. A control function is determined, which includes configuration data for the drives, with at least one control action being performed on the precondition of one or more performance features that are to be achieved in the system model, during which control action the operating data is simulated for a plurality of time steps.

Abstract: A control device for a technical system, state-specific safety information about an admissibility of a control action signal is read in by a safety module is provided. Furthermore, a state signal indicating a state of the technical system is supplied to a machine learning module and to the safety module. In addition, an output signal of the machine learning module is supplied to the safety module. The output signal is converted into an admissible control action signal by the safety module on the basis of the safety information depending on the state signal. Furthermore, a performance for control of the technical system by the admissible control action signal is ascertained, and the machine learning module is trained to optimize the performance. The control device is then configured by the trained machine learning module.

Abstract: Method and device for controlling a machine in accordance with to multiple control objectives in which machine control is based on automated learning of subordinate control skills, wherein the device provides multiple subordinate control skills which are each assigned to a different one of the multiple control objectives, the device provides multiple learning processes that are reinforcement learning processes that are each assigned to a different one of the multiple control objectives and are configured to optimize the corresponding subordinate control skill based on input data received from the machine, and where the device is configured to determine a superordinate control skill based on the subordinate control skills and to control the machine based on the superordinate control skill.

Abstract: A method for designing a turbomachine vane, in which predefined input parameters are transmitted to a neuronal network system and vane parameters are determined and output by the neuronal network system based on the transmitted input parameters. The neuronal network system has several separate neuronal networks each with an output layer, each of which determines one or more of the vane parameters and outputs same via the output layer. A first neuronal network and a second neuronal network belong to the separate neuronal networks of the neuronal network system and the vane parameter(s) which are determined by the first neuronal network and output via the output layer of said neuronal network differ(s) from the vane parameter(s) that are determined by the second neuronal network and are output via the output layer of said neuronal network.

Abstract: Provided is a method for the computer-assisted control of a technical system, in particular in a plant for generating energy, to achieve a predetermined technical behavior of the technical system, wherein an operating data set for controlling the system is provided. A system model for describing the mode of operation of the technical system is provided, wherein on the basis of the operating data set and on the basis of the system model, an optimization data set is determined by an optimization method. Based on the optimization data set, relevant parameters of the technical system that allow a more advantageous control of the technical system than other parameters of the technical system are selected using a selection method, wherein with the selected relevant parameters, a control method for the technical system is determined, wherein the technical system is controlled with the aid of the control method.

Abstract: The invention relates to a method and a device (1) for determining the leading edges (S1, S2) of two overlapping image captures of a surface (OF), comprising at least one camera (2) having a matrix-type sensor (6), having n lines (7), a position and location measuring system (3), an evaluation unit (4) and a storage means (5), wherein an elevation model (H) of the surface (OF) and a projection model (P) of the camera (2) are stored in the storage means (5), which the evaluation unit (4) can access, wherein the camera position (P1, P2) in the first and second image capture is determined by means of the position and location measuring system (3), wherein a horizontal mid-point (M) between the two camera positions (P1, P2) is determined and a projection of the midpoint (M) onto the surface (OF) is carried out, wherein a back projection onto the sensor (6) is carried out in the first and second camera position (P1, P2) by means of the projection model (P) for the point (MO) determined in the above-mentioned manne