Method for training neural networks
AssigneeBayer Technology Services GmbH
InventorsThomas Mrziglod, Georg Mogk
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Abstract
The invention relates to a system and a method for training a number of neural networks, by determining a first training data record, wherein the training data have a particular accuracy, generating a number of second data training records by perturbing the first training data record with a random variable, and training each of the neural networks with one of the training data records. A prognosis and an estimation of the prognosis error can be carried out by means of such a system.
Claims21
A method for training a plurality of neural networks for process optimization, comprising the steps of:
- determining a first training data record, wherein each training datum has a measurement accuracy and wherein the training data are obtained by perturbing the data within an error range of the measurement accuracy;
- generating a plurality of second data training records by perturbing the first training data record with a random variable;
- training each of the plurality of neural networks with one of the training data records;
- and applying the trained neural networks to process optimization.
A method for providing a prognosis by utilizing a plurality of neural networks, comprising the steps of:
- determining a first training data record, each training datum has a measurement accuracy and wherein the training data are obtained by perturbing the data in an error range of the measurement accuracy;
- generating a plurality of second data training records by perturbing the first training data record with a random variable;
- training each of the plurality of neural networks with one of the training data records;
- and
- determining a prognosis value based on an evaluation of the prognoses output by the plurality of neural networks.
A computer system for determining at least one of prognosis value, comprising
- a plurality of neural networks trained by training data records, each training datum has a measurement accuracy and wherein the training data are obtained by perturbing the data in an error range of the measurement accuracy;
- an input means for entering input data into the neural networks,
- an evaluating means for evaluating the prognoses output by the neural networks for determining a prognosis value.
Dependencies are inferred from the claim text.