PurposeThis paper aims to present an alternative airspeed computation method based on artificial neural networks (ANN) without requiring pitot-static system measurements.Design/methodology/approachThe data set used to train proposed neural model is obtained from the Digital Flight Data Acquisition Unit records of a Boeing 737 type commercial aircraft for real flight routes. The proposed method uses the flight parameters as inputs of the ANN. The Levenberg–Marquardt training algorithm was used to train the neural model.FindingsThe predicted airspeed values obtained with ANN are in good agreement with the measured airspeed values. The proposed neural model can be used as an alternative airspeed computation method.Practical implicationsThe proposed alternative airspeed computation method can be used when the air data computer or pitot-static system has failed.Originality/valueThe proposed method uses flight parameters as inputs for the ANN. As such, airspeed is calculated using flight parameters instead of the pitot-static system measurements.
Aircraft Engineering and Aerospace Technology – Emerald Publishing
Published: Mar 5, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera