论文信息 - Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification

Applying Bidirectional Long Short-Term Memories (BLSTM) to Performance Data in Air Traffic Management for System Identification

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.

Stefan Reitmann | Karl Nachtigall | K. Nachtigall | Stefan Reitmann

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