Abstract: Provided is a method for the model-based determination of a system status of a dynamic system by means of a model, wherein: a recurrent neural network is provided as the model of the dynamic system; the model is supplied with a time series of potentially recordable measurement values as an input variable, the values comprising recorded and missing measurement values; at least one system status associated with a time point is generated from the model, from which status at least one target value belonging to the respective time point can be determined; sequential system statuses transition into one other by means of a respective status transition; and a correction of at least one system status is carried out on the basis of the time series with the aid of the status transition.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: A biochemical analytical device and a biochemical analytical method for determining an analyte in a test sample are provided. In the technique, the biochemical analytical device includes a sample port to receive the test sample, a sensor to probe the test sample and to generate sensor data, and a processor. The sensor data corresponds to the analyte in the test sample. The processor receives the sensor data from the sensor and selects a non-linear function for the received sensor data. The processor fits the selected non-linear function to the sensor data. Additionally, the processor compares the fitted non-linear function to a reference data to determine the analyte in the test sample.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: Modeling effectiveness of a verum includes dividing a group of patients into a placebo group and a verum group, defining a plurality of characteristics of the group of patients, and generating a model for the placebo group based on the plurality of characteristics. The method also includes generating a model for the verum group based on the plurality of characteristics, and isolating a placebo effect in the verum group in order to determine a pure verum effect.

Abstract: Disclosed is a method for the computer-assisted modeling of a technical system. One or more output vectors are modeled dependent on one or more input vectors by the learning process of a neural network on the basis of training data of known input vectors and output vectors. Each output vector comprises one or more operating variables of the technical system, and each input vector comprises one or more input variables that influence the operating variable(s). The neural network is a feedforward network with an input layer, a plurality of hidden layers, and an output layer. The output layer comprises a plurality of output clusters, each of which consists of one or more output neurons, the plurality of output clusters corresponding to the plurality of hidden layers. Each output cluster describes the same output vector and is connected to another hidden layer.

Abstract: A method for the computer-assisted control and/or regulation of a technical system is provided. The method includes two steps, namely modeling the dynamic behavior of the technical system with a recurrent neural network using training data, the recurrent neural network includes states and actions determined using a simulation model at different times and learning an action selection rule by the recurrent neural network to a further neural network. The method can be used with any technical system in order to control the system in an optimum computer-assisted manner. For example, the method can be used in the control of a gas turbine.

Abstract: A method for the computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector with one or more observables as entries is provided. The neural network includes both a causal network with a flow of information that is directed forwards in time and a retro-causal network with a flow of information which is directed backwards in time. The states of the dynamic system are characterized by first state vectors in the causal network and by second state vectors in the retro-causal network, wherein the state vectors each contain observables for the dynamic system and also hidden states of the dynamic system. Both networks are linked to one another by a combination of the observables from the relevant first and second state vectors and are learned on the basis of training date including known observables vectors.

Abstract: An approximation is determined for the future system behavior by a similarity analysis using a previously known behavior of the dynamic system, whereupon the future system behavior is determined by using the approximation for the future behavior of the dynamic system as well as a neuronal network structure, especially a causal retro-causal network (causality analysis).

Abstract: An approximation is determined for the future system behavior by a similarity analysis using a previously known behavior of the dynamic system, whereupon the future system behavior is determined by using the approximation for the future behavior of the dynamic system as well as a neuronal network structure, especially a causal retro-causal network (causality analysis).

Abstract: The invention relates to a method for speech processing in which input variables containing speech features are mapped onto output variables. In the mapping process, the input variables are weighted and/or identical maps are produced for different sets of input variables and at least one output variable.

Abstract: A current first state, of a first temporal sequence of respective first states of a dynamically modifiable system, is determined. The first current state of the system is determined by combining a first system-inherent information flow comprising past system information of the system with a second system-inherent information flow comprising future system information in the first current state. The first current state is then determined from the combination.

Abstract: Computation elements are connected to one another with a first subsystem having a first input computation element, to which time series values, which each describe one state of a system in a first state space at a time, can be supplied. The first input computation element is connected to a first intermediate computation element, by which a state of the system can be described in a second state space at a time. In a second subsystem a second intermediate computation element, by which a state of the system can be described in the second state space at a time, is connected to a first output computation element, on which a first output signal can be tapped off. In a third subsystem a third intermediate computation element, by which a state of the system can be described in the second state space at a time, is connected to a second output computation element, on which a second output signal can be tapped off.

Abstract: The invention relates to the computer-assisted mapping of a plurality of temporarily variable status conditions. According to the invention, a first status description in a first state space is mapped onto a second status description in the second state space by mapping, and the second status description of a temporarily later state is taken into consideration during mapping. By carrying out a further mapping, the second state description is mapped back onto a third state description in the first state space.

Abstract: The invention relates to a configuration of interconnected arithmetic elements and to a method for the computer-aided determination of a second state of a system in a first state space from a first state of the system in the first state space. According to the invention, the first state is transformed into a third state of the system in a second state space. A fourth state of the system in the second state space is determined and a variation between the third state and the forth state is ascertained. The second state is determined by using said variation and the first state.

Abstract: An input signal is transformed into a predetermined space. Transformation computer elements are connected to one another such that transformed signals can be taken at the transformation computer elements, whereby at least three transformed signals relate to respectively successive points in time. Composite computer elements are respectively connected to two transformation computer elements. Further, a first output computer element is provided at which an output signal that describes a system status at a point in time can be taken. The first output computer element is connected to the transformation computer elements. Further, a second output computer element is provided that is connected to the composite computer elements and given whose employment a predetermined condition can be taken into consideration when training the arrangement.

Abstract: The invention relates to a method for speech processing in which input variables containing speech features are mapped onto output variables. In the mapping process, the input variables are weighted and/or identical maps are produced for different sets of input variables and at least one output variable.

Abstract: A set of fuzzy rules (FR) is mapped onto a neural network (NN) (501). The neural network (NN) is trained (502), and weights (wi) and/or neurons (NE) of the neural network (NN) are pruned or grown (503). A new neural network (NNN) formed in this way is mapped onto a new fuzzy rule set (NFR) (504).