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• W2895978252 abstract "No AccessEngineering NotesDecision Tree– and Random Forest–Based Novel Unsteady Aerodynamics Modeling Using Flight DataAjit Kumar and Ajoy Kanti GhoshAjit KumarIndian Institute of Technology of Kanpur, Kanpur 208 016, Uttar Pradesh, India*Ph.D. Student, Department of Aerospace Engineering.Search for more papers by this author and Ajoy Kanti GhoshIndian Institute of Technology of Kanpur, Kanpur 208 016, Uttar Pradesh, India†Professor, Department of Aerospace Engineering.Search for more papers by this authorPublished Online:15 Oct 2018https://doi.org/10.2514/1.C035034SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Jategaonkar R. V., Flight Vehicle System Identification: A Time Domain Methodology, AIAA Education Series, AIAA, Reston, VA, 2006, Chaps. 2, 12. LinkGoogle Scholar[2] Grauer J. A. and Morelli E. 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Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byRegularization regression methods for aerodynamic parameter estimation from flight data11 January 2023 | Aircraft Engineering and Aerospace Technology, Vol. 14A Multi-DOF Manipulator Joint Trajectory Tracking and Monitoring Method Based on Decision TreeMathematical Problems in Engineering, Vol. 2022Machine learning in aerodynamic shape optimizationProgress in Aerospace Sciences, Vol. 134Incorporating Physical Models for Dynamic Stall Prediction Based on Machine LearningXu Wang , Jiaqing Kou , Weiwei Zhang and Zhitao Liu21 April 2022 | AIAA Journal, Vol. 60, No. 7Online Learning Behavior Feature Mining Method Based on Decision TreeJournal of Cases on Information Technology, Vol. 24, No. 5Sports Enterprise Marketing and Financial Risk Management Based on Decision Tree and Data MiningJournal of Healthcare Engineering, Vol. 2021On the application of surrogate regression models for aerodynamic coefficient prediction10 March 2021 | Complex & Intelligent Systems, Vol. 7, No. 4Dynamic prediction model of bridge project life cycle cost investment based on decision tree algorithmIOP Conference Series: Earth and Environmental Science, Vol. 760, No. 1Estimation of aerodynamic parameters near stall using maximum likelihood and extreme learning machine-based methods23 October 2020 | The Aeronautical Journal, Vol. 125, No. 1285Empirical Assessment of Deep Gaussian Process Surrogate Models for Engineering ProblemsDushhyanth Rajaram, Tejas G. Puranik, S. Ashwin Renganathan, WoongJe Sung, Olivia Pinon Fischer, Dimitri N. Mavris and Arun Ramamurthy18 September 2020 | Journal of Aircraft, Vol. 58, No. 1Semi-parametric Regression based on Machine Learning Methods for UAS Stall IdentificationIFAC-PapersOnLine, Vol. 54, No. 7 What's Popular Volume 56, Number 1January 2019 CrossmarkInformationCopyright © 2018 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the ISSN 0021-8669 (print) or 1533-3868 (online) to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAerodynamic PerformanceAerodynamicsAeronautical EngineeringAeronauticsAirspeedBoundary LayersComputational Fluid DynamicsFlow RegimesFluid DynamicsNumerical AnalysisUnsteady AerodynamicsVortex DynamicsWind Tunnels KeywordsFlight DataUnsteady AerodynamicsAerodynamic CoefficientsElevator DeflectionArtificial Neural NetworkFlight TestingMachine LearningHysteresisStatistical AnalysisFlow ConditionsPDF Received28 March 2018Accepted24 July 2018Published online15 October 2018" @default.
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