Title
Evolving Neural Network Weights For Time-Series Prediction Of General Aviation Flight Data
Abstract
This work provides an extensive analysis of flight parameter estimation using various neural networks trained by differential evolution, consisting of 12,000 parallel optimization runs. The neural networks were trained on data recorded during student flights stored in the National General Aviation Flight Database (NGAFID), and as such consist of noisy, realistic general aviation flight data. Our results show that while backpropagation via gradient and conjugate gradient descent is insufficient to train the neural networks, differential evolution can provide strong predictors of certain flight parameters (10% over a baseline prediction for airspeed and 70% for altitude), given the four input parameters of airspeed, altitude, pitch and roll. Mean average error ranged between 0.08% for altitude to 2% for roll. Cross validation of the best neural networks indicate that the trained neural networks have predictive power. Further, they have potential to act as overall descriptors of the flights and can potentially be used to detect anomalous flights, even determining which flight parameters are causing the anomaly. These initial results provide a step towards providing effective predictors of general aviation flight behavior, which can be used to develop warning and predictive maintenance systems, reducing accident rates and saving lives.
Year
DOI
Venue
2014
10.1007/978-3-319-10762-2_76
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIII
Keywords
Field
DocType
Time-Series Prediction, Asynchronous Differential Evolution, Neural networks, Flight Prediction, Aviation Informatics
Time series,Computer science,Control theory,Artificial intelligence,Predictive maintenance,Artificial neural network,Flight dynamics,Simulation,Differential evolution,Airspeed,Backpropagation,Cross-validation,Machine learning
Conference
Volume
ISSN
Citations 
8672
0302-9743
4
PageRank 
References 
Authors
0.47
10
4
Name
Order
Citations
PageRank
Travis Desell111618.56
Sophine Clachar2131.23
James Higgins3152.94
Brandon Wild4152.60