Aerodynamic Parameter Estimation for Near-Stall Maneuver Using Neural Networks and Artificial Bee Colony Algorithm-Reference-Cited by-同舟云学术

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Aerodynamic Parameter Estimation for Near-Stall Maneuver Using Neural Networks and Artificial Bee Colony Algorithm

Published:2024-09 Issue:5 Volume:37 Page:
ISSN:0893-1321
Container-title:Journal of Aerospace Engineering
language:en
Short-container-title:J. Aerosp. Eng.

Author:

Kumar Prashant¹^ORCID,Sonkar Sarvesh²,George Riya Catherine³^ORCID,Philip Deepu⁴,Ghosh A. K.⁵

Affiliation:

1. Research Scholar, Dept. of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India. ORCID: .

2. Research Scholar, Dept. of Design, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.

3. Assitant Professor, Dept. of Civil and Environmental Engineering, Hiroshima Univ., Hiroshima 739-8526, Japan (corresponding author). ORCID: .

4. Professor, Dept. of Industrial and Management Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.

5. Professor, Dept. of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh 208016, India.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JAEEEZ.ASENG-4884

Reference42 articles.

1. Nonlinear aircraft system identification using artificial neural networks enhanced by empirical mode decomposition

2. Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

3. Flight Vehicle System Identification: a Time-Domain Methodology – Second edition Progress in Aeronautics and Astronautics Vol 245R. V. Jategaonkar American Institute of Aeronautics and Astronautics, 1801 Alexander Bell Drive, Suite 500, Reston, VA, 20191-4344, USA. 2014. 627pp. Illustrated. $119.95. ISBN 978-1-62410-278-3.

4. Chauhan R. K. and S. Singh. 2018. “Review of aerodynamic parameter estimation techniques.” In Proc. 2017 Int. Conf. on Infocom Technologies and Unmanned Systems: Trends and Future Directions ICTUS 2017 864–869. New York: IEEE. https://doi.org/10.1109/ICTUS.2017.8286127.

5. Cohen D. J. Foley H. Zamani J. Allan and W. B. Croft. 2018. “Universal approximation functions for fast learning to rank: Replacing expensive regression forests with simple feed-forward networks.” In Proc. 41st Int. ACM SIGIR Conf. on Research & Development in Information Retrieval 1017–1020. New York: Association for Computing Machinery. https://doi.org/10.1145/3209978.3210137.

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