The present invention provides a system and method of side-stepping the need to retrain neural network model after initially trained using a simulator by comparing real-world data to data predicted by the simulator for the same inputs, and developing a mapping correlation that adjusts real world data toward the simulation data. Thus, the decision logic developed in the simulation-trained model is preserved and continues to operate in an altered reality. A threshold metric of similarity can be initially provided into the mapping algorithm, which automatically adjusts real world data to adjusted data corresponding to the simulation data for operating the neural network model when the metric of similarity between the real world data and the simulation data exceeds the threshold metric. Updated learning can continue as desired, working in the background as conditions are monitored.
CONTINUOUS LEARNING OF SIMULATION TRAINED DEEP NEURAL NETWORK MODEL FOR FLOATING PRODUCTION PLATFORMS, VESSELS AND OTHER FLOATING SYSTEMS
KONTINUIERLICHES LERNEN EINES TRAINIERTEN TIEFEN NEURONALEN NETZMODELLS FÜR SCHWIMMENDE PRODUKTIONSPLATTFORMEN, SCHIFFE UND ANDERE SCHWIMMENDE SYSTEME
APPRENTISSAGE CONTINU D'UN MODÈLE DE RÉSEAU NEURONAL PROFOND ENTRAÎNÉ PAR SIMULATION POUR PLATEFORMES DE PRODUCTION FLOTTANTES, VAISSEAUX ET AUTRES SYSTÈMES FLOTTANTS
2021-04-14
Patent
Elektronische Ressource
Englisch
Europäisches Patentamt | 2019
|Continuous learning of simulation trained deep neural network model
Europäisches Patentamt | 2022
|