Neural networks to classify atmospheric turbulence from flight test data: an optimization of input parameters for a generic model-Reference-Cited by-同舟云学术

Neural networks to classify atmospheric turbulence from flight test data: an optimization of input parameters for a generic model

Published:2022-02-14 Issue:3 Volume:44 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Oliveira Matheus M.,Mayor Gabriel S.,Macedo Joao Paulo,Bidinotto Jorge H.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,General Engineering,Aerospace Engineering,Automotive Engineering,Industrial and Manufacturing Engineering,Applied Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s40430-022-03386-1.pdf

Reference25 articles.

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3. Press H, Mazelsky B (1953) A study of the application of power-spectral methods of generalized harmonic analysis to gust loads on airplanes, tech. rep., National Advisory Committee for Aeronautics, Washington DC, USA

4. Diederich FW (1954) The response of an airplane to random atmospheric disturbances. Thesis (ph.d.), California Institute of Technology

5. Houbolt JC, Kordes EE (1954) Structural Response to Discrete and Continuous Gusts of an Airplane Having Wind Bending Flexibility and a Correlation of Calculated and Flight Results. tech. rep., National Advisory Committee for Aeronautics, Washington DC, USA

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