Neural Partial Differentiation-Based Estimation of Terminal Airspace Sector Capacity.

Mohamed, Majeed and Rong, S (2021) Neural Partial Differentiation-Based Estimation of Terminal Airspace Sector Capacity. SAE International Journal of Aerospace, 14 (2). p. 1. ISSN 19463855

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Abstract

The main focus of this article is the online estimation of the terminal airspace sector capacity from the Air Tra� c Controller 0ATC) dynamical neural model using Neural Partial Di� erentiation (NPD) with permissible safe separation and a� ordable 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

Item Type: Article
Uncontrolled Keywords: ATC (Air Traffic Controller) workload, Estimation and dynamical modelling, Neural partial differentiation, Output error method, Terminal airspace capacity
Subjects: AERONAUTICS > Air Transportation and Safety
AERONAUTICS > Aircraft Stability and Control
ENGINEERING > Fluid Mechanics and Thermodynamics
Depositing User: Mrs. Usha Kumari
Date Deposited: 01 Aug 2022 11:02
Last Modified: 01 Aug 2022 11:02
URI: http://nal-ir.nal.res.in/id/eprint/13571

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