According to one aspect, uncertainty prediction based deep learning may include receiving, using a memory, a trained neural network policy π trained based on a first dataset in a first environment, implementing, via a controller, the trained neural network policy π in a second environment by receiving an input and generating an output y, calculating an uncertainty array U[T] for a time window T, wherein the uncertainty array is indicative of a level of uncertainty associated with an output sample distribution of the output across the time window T based on a temporal divergence, an entropy H, a variational ratio VR, and a standard deviation SD of the output y, and executing, via the controller and one or more systems, an action based on the uncertainty array U[T], such as discontinuing use of the trained neural network policy π.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    UNCERTAINTY PREDICTION BASED DEEP LEARNING


    Beteiligte:
    CUI YUCHEN (Autor:in) / ISELE DAVID FRANCIS (Autor:in) / FUJIMURA KIKUO (Autor:in)

    Erscheinungsdatum :

    19.03.2020


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G05D SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES , Systeme zum Steuern oder Regeln nichtelektrischer veränderlicher Größen / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    Uncertainty prediction based deep learning

    CUI YUCHEN / ISELE DAVID FRANCIS / FUJIMURA KIKUO | Europäisches Patentamt | 2022

    Freier Zugriff




    Deep Learning Based Missile Trajectory Prediction

    Wang, Zijian / Zhang, Jinze / Wei, Wei | IEEE | 2020