Optimizing neural networks for enhancing air traffic security
Name
56960613-MIT.pdf
Description
Full printable version
Size
8.3 MB
Format
Adobe PDF
Checksum (MD5)
9f8127eb0b40af7230e78a93fe4ba07a
Author(s)
Cooney, Geoffrey T. (Geoffrey Thomas), 1980-
Advisor(s)
Amar Gupta.
Alternative Title
Building an optimized neural network for enhancing air safety
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
This thesis contains the process and results related to optimizing a neural network to predict future positions of airplanes in the vicinity of airports. These predicted positions are then used to calculate future separation distances between pairs of airplanes. The predicted values of the separation distance are used to ensure adequate distances between adjacent aircrafts in the air and, if necessary, to create early warning alarms to alert air traffic control tower personnel about planes that may pass too near each other in the immediate future. The thesis covers three areas of work on this topic. The first section involves optimizing a neural network for Chicago O'Hare Airport. The second is related to gathering data on the performance of this network in different scenarios. These data can be used to determine if the different days/runways have different characteristics. The final phase of this document describes how to generalize the process used to build an optimized neural network for Chicago O'Hare airport in order to provide the capability to easily recreate the process for another airport.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 81-83).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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