Abstract
The performance analysis of complex systems like Air Traffic Management (ATM) is a challenging task. To overcome statistical complexities through analyzing non-linear time series we approach the problem with machine learning methods. Therefore we understand ATM (and its identified system model) as a system of coupled and interdependent sub-systems working in time-continuous processes, measurable through time-discrete time series.
In this paper we discuss the requirements of a system identification process and the attached statistical analysis of ATM emitted performance data based on discussed benchmarking frameworks. The superior aim is to show, that neural networks are able to handle complex non-linear time-series, to learn how to rebuild them considering multidimensional inputs and to store knowledge about the observation data set’s behavior.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Parmenter, D.: Key Performance Indicators - Developing, Implementing and Using Winning KPIs, p. 3. Wiley, Hoboken (2007)
Reitmann, S., Gillissen, A., Schultz, M.: Performance benchmarking in interdependent ATM systems. In: International Conference on Research in Air Transportation, Philadelphia, USA (2016)
ICAO: Manual on Global Performance of the Air Navigation System, Doc 9883, 1st edn. (2009)
Airports Council International: Guide to Airport Performance Measures, pp. 6–16 (2012)
Reitmann, S., Nachtigall, K.: Pattern recognition and prediction of multivariate time series with long short-term memory (LSTM). In: International Science & Progress Conference, St. Petersburg, Russia (2016)
Hochreiter, S., Schmidhuber, J.: Long short term-memory. Neural Comput. 9(8), 1735–1780 (1997)
Hochreiter, S., Schmidhuber, J.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451–2471 (2000)
Graves, A., Jaitly, N., Mohamed, A.: Hybrid speech recognition with deep bidirectional LSTM. In: Automatic Speech Recognition and Understanding (ASRU) (2013)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architecures. Neural Netw. 18(5), 602–610 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Reitmann, S., Nachtigall, K. (2017). Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-68612-7_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68611-0
Online ISBN: 978-3-319-68612-7
eBook Packages: Computer ScienceComputer Science (R0)