Abstract
Reliably recognising what a helicopter is doing in flight is an important goal in the helicopter R&D community. In the work described, machine learning methods were applied to the problem of recognising level flight conditions (i.e. from hover to high-speed forward flight). The approach taken was to use an absolute minimum of information; in this case, analysing the data obtained from single, airframe-mounted strain gauge. The results revealed that flight condition recognition was possible with this minimalist approach, but that more work needs to be done to decrease the error rates, thereby increasing the recognition reliability.
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Abbreviations
- ANN:
-
Artificial Neural Network
- C4.5:
-
Program which implements the Decision Tree algorithm
- PSD:
-
Power Spectral Density
- SNNS:
-
Stuttgart Neural Network Simulator
- SSE:
-
Sum-Squared Error
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© 1998 Springer-Verlag Berlin Heidelberg
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Lombardo, D.C. (1998). Helicopter flight condition recognition: A minimalist approach. In: Antoniou, G., Slaney, J. (eds) Advanced Topics in Artificial Intelligence. AI 1998. Lecture Notes in Computer Science, vol 1502. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095053
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DOI: https://doi.org/10.1007/BFb0095053
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