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Neural Partial Differentiation-Based Estimation of Terminal Airspace Sector Capacity

Journal Article
01-14-02-0010
ISSN: 1946-3855, e-ISSN: 1946-3901
Published July 14, 2021 by SAE International in United States
Neural Partial Differentiation-Based Estimation of Terminal Airspace Sector Capacity
Sector:
Citation: Mohamed, M. and Rong, S., "Neural Partial Differentiation-Based Estimation of Terminal Airspace Sector Capacity," SAE Int. J. Aerosp. 14(2):2021, https://doi.org/10.4271/01-14-02-0010.
Language: English

Abstract:

The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Traffic Controller 0ATC) dynamical neural model using Neural Partial Differentiation (NPD) with permissible safe separation and affordable workload. For this purpose, a primarily neural model of a multi-input-single-output (MISO) ATC dynamical system is established, and the NPD method is used to estimate the model parameters from the experimental data. These estimated parameters have a less relative standard deviation, and hence the model validation results show that the predicted neural model response is well matched with the intervention of the ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters, which are unknown in practice.