Siemens Aktiengesellschaft

Panoratio Database Images Gmbh

Reimar Hofmann

Volker Tresp

Michael Haft

Volkmar Sterzing

The invention relates to sensitivity analysis of variables influencing a combustion process. A trainable, statistical model is trained in such a way that it describes the combustion process in the combustion chamber. The trained statistical model is used to determine the influence of the variables on said combustion process in the combustion chamber.

Method and computer program for analyzing variables using pruning, influencing a combustion process in a combustion chamber, using a trainable statistical model

A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.

Statistical models for improving the performance of database operations

The invention relates to a method and a system for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least on e of the state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by using the sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Method and system for modeling a system

In a method for tranining a neural network with the non-deterministic behavior of a technical system, weightings for the neurons of the neural network are set during the training using a cost function. The cost function evaluates a beneficial system behavior of the technical system to be modeled, and thereby intensifies or increases the weighting settings which contribute to the beneficial system behavior, and attenuates or minimizes weightings which produce a non-beneficial behavior. Arbitrary or random disturbances are generated by disturbing the manipulated variable with noise having a known noise distribution, these random disturbances significantly faciliating the mathematical processing of the weightings which are set, because the terms required for that purpose are simplified. The correct weighting setting for the neural network is thus found on the basis of a statistical method and the application of a cost function to the values emitted by the technical system or its model.

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Munich, Germany

Reimar Hofmann