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1. 发明申请

US20220335281A1 DEVICE AND IN PARTICULAR COMPUTER-IMPLEMENTED METHOD FOR VERIFICATION 有权
公开(公告)号：US20220335281A1
公开(公告)日：2022-10-20
申请号：US17658843
申请日：2022-04-12
申请人： Robert Bosch GmbH
发明人： Thomas Spieker , Ernst Kloppenburg , Konrad Groh
IPC分类号： G06N3/063 , G06N3/08
摘要： A device and a method, in particular a computer-implemented method, for the verification of an artificial neural network that is trained to map an input point from an input space of a function, in particular a limited or Lipschitz-constant function, as accurately as possible onto a functional value of the function. A test point is specified, the test point including a pair of a test input point from the input space of the function and a test functional value, the input point being determined from the input space, the input point being mapped by the artificial neural network onto the functional value, a reference for the functional value being determined using the test input point, a deviation of the functional value from the reference being determined, and a measure of a susceptibility to error of the artificial neural network being determined as a function of the deviation.

2. 发明申请

US20190042980A1 METHOD AND DEVICE FOR GENERATING A MACHINE LEARNING SYSTEM AND VIRTUAL SENSOR DEVICE 审中-公开
公开(公告)号：US20190042980A1
公开(公告)日：2019-02-07
申请号：US16050407
申请日：2018-07-31
申请人： Robert Bosch GmbH
发明人： Volker Imhof , Ernst Kloppenburg , Heiner Markert
IPC分类号： G06N99/00 , G06K9/62 , G06N7/08
摘要： A method for automatically generating a machine learning system which ascertains as a function of an input variable time series an output variable time series approximating an actual output variable time series, the machine learning system ascertaining a value of the output variable assigned to the predefinable point in time as a function of input and output variable values at the points in time, which are in a predefinable time interval, prior to a predefinable point in time, only a subset of the values of the input variable within the interval and of the values of the output variable being incorporated when ascertaining the output variable assigned to the predefinable point in time, the subset being selected so that it includes available input variable values and the output variable values assigned to the points in time within the interval, which are in a predefinable equidistant selection raster within the interval.

3. 发明申请

US20160300152A1 METHOD AND DEVICE FOR DETERMINING A DATA-BASED FUNCTIONAL MODEL 审中-公开
标题翻译：用于确定基于数据的功能模型的方法和装置
公开(公告)号：US20160300152A1
公开(公告)日：2016-10-13
申请号：US15100432
申请日：2014-10-13
申请人： ROBERT BOSCH GMBH
发明人： Ernst Kloppenburg
IPC分类号： G06N7/00 , G06N99/00
CPC分类号： G06N7/005 , G06N20/00
摘要： A method for creating a Gaussian process model as a data-based functional model for an output quantity that is to be modeled, based on training data in an input data space, including providing training data having training data points and output values, assigned to the training data points, of one or more output quantities; ascertaining a point density that is a function of the position of the training data points in the input data space; ascertaining a length scale function for each input quantity of the training data as a function of the point density; and generating a Gaussian process model from the training data and the output data of the output quantity to be modeled, based on the ascertained length scale functions.
摘要翻译：一种用于基于输入数据空间中的训练数据创建高斯过程模型作为用于要被建模的输出量的基于数据的功能模型的方法，包括提供具有训练数据点和输出值的训练数据，分配给训练数据点，一个或多个输出量; 确定作为输入数据空间中的训练数据点的位置的函数的点密度; 确定作为点密度的函数的训练数据的每个输入量的长度尺度函数; 并根据所确定的长度尺度函数从训练数据和待建模的输出量的输出数据生成高斯过程模型。

4. 发明授权

US11537090B2 Model calculating unit and control unit for selectively calculating an RBF model, a gaussian process model and an MLP model 有权
公开(公告)号：US11537090B2
公开(公告)日：2022-12-27
申请号：US16466731
申请日：2017-09-04
申请人： Robert Bosch GmbH
发明人： Andre Guntoro , Ernst Kloppenburg , Heiner Markert , Martin Schiegg
IPC分类号： G06N3/063 , G05B13/02 , F02P5/15 , G06N7/00
摘要： A model calculating unit for the selective calculation of an RBF model or of a neural layer of a multilayer perceptron model having a hardwired processor core designed in hardware for calculating a fixedly specified computing algorithm in coupled function blocks. The processor core is designed to calculate an output variable for an RBF model as a function of one or multiple input variables of an input variable vector, of supporting points, of length scales, of parameters specified for each supporting point, the processor core furthermore being designed to calculate an output variable for each neuron for the neural layer of the multilayer perceptron model having a number of neurons as a function of the one or the multiple input variables of the input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron.

5. 发明申请

US20190310590A1 MODEL CALCULATING UNIT AND CONTROL UNIT FOR SELECTIVELY CALCULATING AN RBF MODEL, A GAUSSIAN PROCESS MODEL AND AN MLP MODEL 审中-公开
公开(公告)号：US20190310590A1
公开(公告)日：2019-10-10
申请号：US16466731
申请日：2017-09-04
申请人： Robert Bosch GmbH
发明人： Andre Guntoro , Ernst Kloppenburg , Heiner Markert , Martin Schiegg
IPC分类号： G05B13/02 , G06N3/063
摘要： A model calculating unit for the selective calculation of an RBF model or of a neural layer of a multilayer perceptron model having a hardwired processor core designed in hardware for calculating a fixedly specified computing algorithm in coupled function blocks. The processor core is designed to calculate an output variable for an RBF model as a function of one or multiple input variables of an input variable vector, of supporting points, of length scales, of parameters specified for each supporting point, the processor core furthermore being designed to calculate an output variable for each neuron for the neural layer of the multilayer perceptron model having a number of neurons as a function of the one or the multiple input variables of the input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron.

6. 发明授权

US09934197B2 Method and device for providing a sparse Gaussian process model for calculation in an engine control unit 有权
公开(公告)号：US09934197B2
公开(公告)日：2018-04-03
申请号：US14581352
申请日：2014-12-23
申请人： Robert Bosch GmbH
发明人： Ernst Kloppenburg , Michael Hanselmann , Heiner Markert , Felix Streichert
IPC分类号： G06F17/10 , G06F17/17 , G06F17/16
CPC分类号： G06F17/10 , G05B2219/40336 , G06F17/16 , G06F17/175
摘要： A method for determining a sparse Gaussian process model to be carried out in a solely hardware-based model calculation unit includes: providing supporting point data points, a parameter vector based thereon, and corresponding hyperparameters; determining or providing virtual supporting point data points for the sparse Gaussian process model; and determining a parameter vector Qy* for the sparse Gaussian process model with the aid of a Cholesky decomposition of a covariant matrix KM between the virtual supporting point data points and as a function of the supporting point data points, the parameter vector based thereon, and the corresponding hyperparameters, which define the sparse Gaussian process model.

7. 发明授权

US11645499B2 Model calculating unit and control unit for calculating a neural layer of a multilayer perceptron model 有权
公开(公告)号：US11645499B2
公开(公告)日：2023-05-09
申请号：US16466814
申请日：2017-09-04
申请人： Robert Bosch GmbH
发明人： Andre Guntoro , Ernst Kloppenburg , Heiner Markert , Martin Schiegg
IPC分类号： G06N20/00 , G06N3/048 , F02D41/26 , G05B13/02 , G06N3/063 , G06N3/08
CPC分类号： G06N3/048 , F02D41/266 , G05B13/027 , G06N3/063 , G06N3/08
摘要： A model calculating unit for calculating a neural layer of a multilayer perceptron model having a hardwired processor core developed in hardware for calculating a definitely specified computing algorithm in coupled functional blocks. The processor core is designed to calculate, as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron, an output variable for each neuron for a neural layer of a multilayer perceptron model having a number of neurons, a sum of the values of the input variables weighted by the weighting factor, determined by the neuron and the input variable, and the offset value specified for the neuron being calculated for each neuron and the result being transformed using an activation function in order to obtain the output variable for the neuron.

8. 发明授权

US11151475B2 Method and device for generating a machine learning system and virtual sensor device 有权
公开(公告)号：US11151475B2
公开(公告)日：2021-10-19
申请号：US16050407
申请日：2018-07-31
申请人： Robert Bosch GmbH
发明人： Volker Imhof , Ernst Kloppenburg , Heiner Markert
IPC分类号： G01H1/00 , G09G5/00 , H04B7/00 , G06N5/02 , G06N20/00 , G06K9/62 , G06N7/08
摘要： A method for automatically generating a machine learning system which ascertains as a function of an input variable time series an output variable time series approximating an actual output variable time series, the machine learning system ascertaining a value of the output variable assigned to the predefinable point in time as a function of input and output variable values at the points in time, which are in a predefinable time interval, prior to a predefinable point in time, only a subset of the values of the input variable within the interval and of the values of the output variable being incorporated when ascertaining the output variable assigned to the predefinable point in time, the subset being selected so that it includes available input variable values and the output variable values assigned to the points in time within the interval, which are in a predefinable equidistant selection raster within the interval.

9. 发明申请

US20210295133A1 MODEL CALCULATING UNIT AND CONTROL UNIT FOR CALCULATING A NEURAL LAYER OF A MULTILAYER PERCEPTRON MODEL 有权
公开(公告)号：US20210295133A1
公开(公告)日：2021-09-23
申请号：US16466814
申请日：2017-09-04
申请人： Robert Bosch GmbH
发明人： Andre Guntoro , Ernst Kloppenburg , Heiner Markert , Martin Schiegg
IPC分类号： G06N3/04 , G06N3/08 , G06N3/063 , G05B13/02 , F02D41/26
摘要： A model calculating unit for calculating a neural layer of a multilayer perceptron model having a hardwired processor core developed in hardware for calculating a definitely specified computing algorithm in coupled functional blocks. The processor core is designed to calculate, as a function of one or multiple input variables of an input variable vector, of a weighting matrix having weighting factors and an offset value specified for each neuron, an output variable for each neuron for a neural layer of a multilayer perceptron model having a number of neurons, a sum of the values of the input variables weighted by the weighting factor, determined by the neuron and the input variable, and the offset value specified for the neuron being calculated for each neuron and the result being transformed using an activation function in order to obtain the output variable for the neuron.

10. 发明授权

US10402739B2 Method and device for determining a data-based functional model 有权
公开(公告)号：US10402739B2
公开(公告)日：2019-09-03
申请号：US15100432
申请日：2014-10-13
申请人： Robert Bosch GmbH
发明人： Ernst Kloppenburg
IPC分类号： G06N20/00 , G06N7/00
摘要： A method for creating a Gaussian process model as a data-based functional model for an output quantity that is to be modeled, based on training data in an input data space, including providing training data having training data points and output values, assigned to the training data points, of one or more output quantities; ascertaining a point density that is a function of the position of the training data points in the input data space; ascertaining a length scale function for each input quantity of the training data as a function of the point density; and generating a Gaussian process model from the training data and the output data of the output quantity to be modeled, based on the ascertained length scale functions.

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