Patent ID: 9466032
Filing Date: 2016-10-11
CPC Classification: F03D,G05B,G06N,Y02E

Claim Text:
1. A method for generation of a model configured for at least one of automated monitoring, forecasting, and regulatory tasks of a technical system, wherein the model is generated based on training data that comprise a plurality of data records, each data record representing a plurality of operating variables for the technical system, the method comprising: generating with a computer the model by 1) learning a data-driven model based on the training data; 2) learning a density estimator based on the training data in parallel with learning the data driven model; 3) outputting by the density estimator a confidence measure for all the data records, said confidence measure for a data record being higher when the data record is more similar to other data records from the training data, similarity being determined based on distances of data records in relation to one another in a data space; 4) reproducing by the data-driven model data records with model errors; 5) performing weighted sampling on the data records based on the confidence measures and the model errors, the data records with low confidence measures and high model errors being weighted relatively higher; 6) obtaining a new subset of data records from the weighted sampling; and 7) repeating steps 1 to 6 until a termination criterion is met.