US 12,333,390 B2
Computer implemented machine learning system and a method for operating the machine learning system for determining a time series
Martin Schiegg, Korntal-Muenchingen (DE); and Muhammad Bilal Zafar, Berlin (DE)
Assigned to ROBERT BOSCH GMBH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Dec. 30, 2020, as Appl. No. 17/138,685.
Claims priority of application No. 20155185 (EP), filed on Feb. 3, 2020.
Prior Publication US 2021/0241174 A1, Aug. 5, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 17/18 (2006.01); G06N 3/045 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 17/18 (2013.01); G06N 3/045 (2023.01)] 12 Claims
OG exemplary drawing
 
1. A computer implemented method of operating a machine learning system for determining a time series, the method comprising the following steps:
providing an input for a first generative model depending on a probabilistic variable, the probabilistic variable being noise;
determining an output of the first model in response to the input for the first model, wherein the output of the first model represents the time series, the first model including a first layer that is trained to map the input for the first model determined depending on the probabilistic variable to output characterizing intermediate data, the first model further including a second layer that is trained to map the intermediate data to the time series depending on an output of a third layer of the first model, the output of the third layer characterizing a physical constraint to a machine state, and wherein values of the time series or the intermediate data are constrained by the output of the third layer;
providing the input for the first model depending on a conditional variable;
wherein the first layer and/or the second layer are trained to map the input for the first model determined depending on the conditional variable and the probabilistic variable to output characterizing the intermediate data;
providing the conditional variable as a continuous or discrete first series of values over time within a time period and/or providing the probabilistic variable as a continuous or discrete second series of values over time within the time period;
determining by the first model, a continuous or discrete third series of values for the time series depending on the values of the first series and the second series; and
determining by a second model a score depending on the values of the first series and the third series; and
wherein the first model is a first Recurrent Neural network and the second model is a second Recurrent Neural network.