Patent application number | Description | Published |
20140309754 | METHOD AND DEVICE FOR CREATING A DATA-BASED FUNCTION MODEL - A method for generating a data-based function model includes: providing a first data-based partial model ascertained from a first training data record; providing at least one additional training data record; and performing the following steps for the at least one additional training data record: ascertaining a difference training data record having training data which correspond to the differences between the output values of the relevant additional training data record and the function value of the sum of the partial function values (f | 10-16-2014 |
20140310210 | METHOD AND DEVICE FOR CREATING A FUCNTION MODEL FOR A CONTROL UNIT OF AN ENGINE SYSTEM - A computerized method for creating a function model based on a non-parametric, data-based model, e.g., a Gaussian process model, includes: providing training data including measuring points having one or multiple input variables, the measuring points each being assigned an output value of an output variable; providing a basic function; modifying the training data with the aid of difference formation between the function values of the basic function and the output values at the measuring points of the training data; creating the data-based model based on the modified training data; and providing the function model as a function of the data-based model and the basic function. | 10-16-2014 |
20140310211 | Method and device for creating a nonparametric, data-based function model - A method for creating a nonparametric, data-based function model having measuring points in multiple training data records, including the following: providing weighting specifications for the measuring points of each training data record; forming a set union of the measuring points of the multiple training data records; and creating the nonparametric function model from the set union of the measuring points of the training data records according to an algorithm which is dependent on the weighting specifications for the measuring points of the multiple training data records. | 10-16-2014 |
20140310212 | METHOD AND DEVICE FOR CREATING A NONPARAMETRIC, DATA-BASED FUNCTION MODEL - A method for ascertaining a nonparametric, data-based function model, in particular a Gaussian process model, using provided training data, the training data including a number of measuring points which are defined by one or multiple input variables and which each have assigned output values of at least one output variable, including: selecting one or multiple of the measuring points as certain measuring points or adding one or multiple additional measuring points to the training data as certain measuring points; assigning a measuring uncertainty value of essentially zero to the certain measuring points; and ascertaining the nonparametric, data-based function model according to an algorithm which is dependent on the certain measuring points of the modified training data and the measuring uncertainty values assigned in each case. | 10-16-2014 |
20140310325 | MODEL CALCULATION UNIT, CONTROL UNIT AND METHOD FOR CALIBRATING A DATA-BASED FUNCTION MODEL - A model calculation unit for calculating a data-based function model in a control unit is provided, the model calculation unit having a processor core which includes: a multiplication unit for carrying out a multiplication on the hardware side; an addition unit for carrying out an addition on the hardware side; an exponential function unit for calculating an exponential function on the hardware side; a memory in the form of a configuration register for storing hyperparameters and node data of the data-based function model to be calculated; and a logic circuit for controlling, on the hardware side, the calculation sequence in the multiplication unit, the addition unit, the exponential function unit and the memory in order to ascertain the data-based function model. | 10-16-2014 |
20140351193 | METHOD AND DEVICE FOR POST-ADAPTION OF A DATA-BASED FUNCTION MODEL - A method for post-adaption of an at least partially data-based function model which corresponds to a sum of a basis function model, e.g., a data-based basis function model, and an additive fault model, includes: providing the basis function model; recording training data; ascertaining the data-based additive fault model based on difference training data which represent differences between the measured values of the training data and the function values of the data-based basis function model at the measuring points of the training data; | 11-27-2014 |
20150012575 | Method for operating a control unit and a control unit having a model calculation unit - A method for operating a control unit, the control unit including a software-controlled main processing unit, a strictly hardware-based model calculation unit for calculating an algorithm, for carrying out a Bayesian regression method, based on configuration data, and a memory unit, a model memory area being defined in the memory unit to which a configuration register block for providing the configuration data in the model calculation unit is assigned, a calculation start-configuration register being assigned the highest address in the configuration register block into which configuration data are written, the writing into of which starts the calculation in the model calculation unit, the configuration data being written in a memory area of the memory unit from the model memory area into the configuration register block with an incremental copying process, the addresses being copied in the incremental copying process in ascending order. | 01-08-2015 |
20150019464 | method and apparatus for supplying interpolation point data for a data-based function model calculation unit - A method for identifying a set of interpolation point data points from training data for a sparse Gaussian process model, encompassing the following tasks: successively selecting training data points from the set of training data for acceptance into or exclusion from a set of interpolation point data points in accordance with a selection criterion; and terminating selection when a termination criterion exists; the selection criterion depending on a divergence between a target value of the selected training data point and a function value, at the selected training data point, of the Gaussian process model based on the respectively current set of interpolation point data points. | 01-15-2015 |