Abstract: A method for determining safety-critical output values by way of a data analysis device for a technical entity. The method includes receiving data and/or measured values for the entity by way of the data analysis device. The method further includes processing the input values (x) by way of the data analysis device using a software application in order to determine at least one first output value (y1). The method further includes using a neural network having a plurality of layers (h?(x)) with first learnable parameters (?); modifying a layer of the neural network using a function (?) so that the output value (y1) is located within a defined value range (C(s)) of at least one target parameter (s); and determining the target parameter (s) and/or the value range (C(s)) of the target parameter (s) using further additional layers (k?(x)) of the neural network with second learnable parameters (?).

Abstract: A method for generating one or more safety-critical output values (y) of an entity, including determining at least one parameter (s) of the entity, whereby the parameter describes at least one state and/or at least one feature of the entity. At least one delimited value range (C(s)) for the parameter (s) is set. At least one measured value (x) for the determined parameter (s) by at least one sensor is ascertained. The measured value (x) from the sensor to at least one processor and/or at least one data storage unit is transmitted. At least one output value (y) is calculated using the processor from the measured value (x) by way of a software application including a neural network (NN) with dynamic restrictions which outputs the output value (y) within the set value range (C(s)) for the determined parameter (s).

Abstract: A method for determining safety-critical output values by way of a data analysis device for a technical entity. The method includes receiving data and/or measured values for the entity by way of the data analysis device. The method further includes processing the input values (x) by way of the data analysis device using a software application in order to determine at least one first output value (y1). The method further includes using a neural network having a plurality of layers (h?(x)) with first learnable parameters (?); modifying a layer of the neural network using a function (?) so that the output value (y1) is located within a defined value range (C(s)) of at least one target parameter (s); and determining the target parameter (s) and/or the value range (C(s)) of the target parameter (s) using further additional layers (k?(x)) of the neural network with second learnable parameters (?).

Abstract: A method for generating one or more safety-critical output values (y) of an entity, including determining at least one parameter (s) of the entity, whereby the parameter describes at least one state and/or at least one feature of the entity. At least one delimited value range (C(s)) for the parameter (s) is set. At least one measured value (x) for the determined parameter (s) by at least one sensor is ascertained. The measured value (x) from the sensor to at least one processor and/or at least one data storage unit is transmitted. At least one output value (y) is calculated using the processor from the measured value (x) by way of a software application including a neural network (NN) with dynamic restrictions which outputs the output value (y) within the set value range (C(s)) for the determined parameter (s).