Methods and Systems For Generating Interpretable and Differentiable Models For Industrial Optimization
개요
발명자
Sven Serneels
IPC 분류
CPC 분류
Embodiments create models configured to predict behavior of real-world systems. An example embodiment receives input and output data for a real-world system and, next, subdivides the input and output data received into a plurality of subsets in accordance with a criterion. For each subset of the plurality, a regression model is fit to data of the subset. For each data point in each subset of the plurality of subsets, a respective weight is assigned to the data point for each regression model. In turn, the model configured to predict the behavior of the real-world system is generated by calculating a weighted average of each regression model using the assigned respective weights.
원문 (중국어)
Embodiments create models configured to predict behavior of real-world systems. An example embodiment receives input and output data for a real-world system and, next, subdivides the input and output data received into a plurality of subsets in accordance with a criterion. For each subset of the plurality, a regression model is fit to data of the subset. For each data point in each subset of the plurality of subsets, a respective weight is assigned to the data point for each regression model. In turn, the model configured to predict the behavior of the real-world system is generated by calculating a weighted average of each regression model using the assigned respective weights.