We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network's utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection


    Beteiligte:


    Erscheinungsdatum :

    2021-03-06


    Format / Umfang :

    7037523 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    System-Level Design Framework for Insertion-Loss-Minimized Optical Network-on-Chip Router Architectures

    Jae, H.L. / Jae-Chern, Y. / Tae, H.H. | British Library Online Contents | 2014


    Handwritten Mathematical Symbol Recognition using Neural Network Architectures

    Padmanandam, Kayal / Yadav, Alekhya / Aishwarya et al. | IEEE | 2022


    Alternate Neural Network Architectures for Beam Vibration Minimization

    Flanders, S. W. / Burke, L. I. / Yalcintas, M. | British Library Online Contents | 1994