Method and apparatus for supplying interpolation point data for a data-based function model calculation unit

An engine control module apparatus for identifying from training data a set of interpolation point data points for implementing a sparse Gaussian process model, comprising:

• a memory unit;
• a model calculation unit;
• and
• a main unit having a processor arrangement configured to perform the following:
• successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points;
• and
• terminating selection when a termination criterion exists;
• wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the sparse Gaussian process model based on the respectively current set of interpolation point data points;
• wherein the model calculation unit is hard-wired or uses a restricted, specialized instruction set, and
• wherein the sparse Gaussian model enables a model value to be determined by the main unit in the engine control module,
• wherein the training data points are of at least variable that describes a physical behavior of an internal combustion engine,
• wherein at least one of the following is satisfied:
• (i) the successive selection of the training data points from a set of training data for acceptance into the set of interpolation point data points in accordance with the selection criterion provides that in each selection cycle the selection criterion identifies for acceptance into the set of interpolation point data points a particular training data point having a target value which maximally diverges from a function value of the sparse Gaussian process model defined by a previously determined interpolation point data, and (ii) the successive selection of training data points from the set of training data for exclusion from the set of interpolation point data points in accordance with the selection criterion that includes the fact that in each selection cycle the selection criterion identifies, for exclusion from the set of interpolation point data points, that training data point whose target value diverges minimally from the function value of the sparse Gaussian process model defined by the previously determined interpolation point data,
• wherein hyperparameters and the interpolation point data are stored in the memory unit,
• wherein the model calculation unit is configured only for carrying out a specific calculation protocol that is based on repeated calculations of a sum, a multiplication, and/or an exponential function, and
• wherein the hyperparameters are for the Gaussian model and are determined based on the interpolation point data.

A system, comprising:

• an engine control module apparatus for identifying from training data a set of interpolation point data points for implementing a sparse Gaussian process model, including:
• a memory unit;
• a model calculation unit;
• and
• a main unit having a processor arrangement configured to perform the following:
• successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points;
• and
• terminating selection when a termination criterion exists;
• wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the sparse Gaussian process model based on the respectively current set of interpolation point data points;
• wherein the engine control module is configured to receive the interpolation point data and calculate the sparse Gaussian process model;
• wherein the model calculation unit is hard-wired or uses a restricted, specialized instruction set, and
• wherein the sparse Gaussian model enables a model value to be determined by the main unit in the engine control module,
• wherein the training data points are of at least variable that describes a physical behavior of an internal combustion engine,
• wherein at least one of the following is satisfied:
• (i) the successive selection of the training data points from a set of training data for acceptance into the set of interpolation point data points in accordance with the selection criterion provides that in each selection cycle the selection criterion identifies for acceptance into the set of interpolation point data points a particular training data point having a target value which maximally diverges from a function value of the sparse Gaussian process model defined by a previously determined interpolation point data, and (ii) the successive selection of training data points from the set of training data for exclusion from the set of interpolation point data points in accordance with the selection criterion that includes the fact that in each selection cycle the selection criterion identifies, for exclusion from the set of interpolation point data points, that training data point whose target value diverges minimally from the function value of the sparse Gaussian process model defined by the previously determined interpolation point data,
• wherein hyperparameters and the interpolation point data are stored in the memory unit,
• wherein the model calculation unit is configured only for carrying out a specific calculation protocol that is based on repeated calculations of a sum, a multiplication, and/or an exponential function, and
• wherein the hyperparameters are for the Gaussian model and are determined based on the interpolation point data.

An electronic control device, comprising:

• a computer readable medium having a computer program, which is executable by a processor, including a program code arrangement having program code for identifying from training data a set of interpolation point data points for a sparse Gaussian process model, by performing the following:
• successively selecting from the set of training data, in accordance with a selection criterion, training data points for acceptance into or exclusion from a set of interpolation point data points;
• and
• terminating selection when a termination criterion exists;
• wherein the selection criterion depends on a divergence between a target value of the training data point to be selected and a function value, at the training data point to be selected, of the sparse Gaussian process model based on the respectively current set of interpolation point data points;
• wherein the model calculation unit is hard-wired or uses a restricted, specialized instruction set, and
• wherein the sparse Gaussian model enables a model value to be determined by the main unit in the engine control module,
• wherein the training data points are of at least variable that describes a physical behavior of an internal combustion engine,
• wherein at least one of the following is satisfied:
• (i) the successive selection of the training data points from a set of training data for acceptance into the set of interpolation point data points in accordance with the selection criterion provides that in each selection cycle the selection criterion identifies for acceptance into the set of interpolation point data points a particular training data point having a target value which maximally diverges from a function value of the sparse Gaussian process model defined by a previously determined interpolation point data, and (ii) the successive selection of training data points from the set of training data for exclusion from the set of interpolation point data points in accordance with the selection criterion that includes the fact that in each selection cycle the selection criterion identifies, for exclusion from the set of interpolation point data points, that training data point whose target value diverges minimally from the function value of the sparse Gaussian process model defined by the previously determined interpolation point data,
• wherein hyperparameters and the interpolation point data are stored in the memory unit,
• wherein the model calculation unit is configured only for carrying out a specific calculation protocol that is based on repeated calculations of a sum, a multiplication, and/or an exponential function, and
• wherein the hyperparameters are for the Gaussian model and are determined based on the interpolation point data.