Unsteady aerodynamics modeling using SVM and artificial neural network

Yichao Jiang, Qingjie Zhao*, Jihong Zhu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Recently, more and more attention has been drawn by the aircraft’s maneuvering problem. This problem is very significant for improving performance of the nonlinear and unsteady modeling methods used for aircrafts at high angles of attack. In this paper, support vector machine (SVM) and artificial neural network are introduced into unsteady aerodynamics modeling. The experimental results show that the generality and precision have been significantly improved using these two methods, which verifies that machine learning methods can be applied to unsteady aerodynamic modeling.

Original languageEnglish
Title of host publicationProceedings of the 2015 Chinese Intelligent Automation Conference - Intelligent Information Processing
EditorsZhidong Deng, Hongbo Li
PublisherSpringer Verlag
Pages577-585
Number of pages9
ISBN (Print)9783662464687
DOIs
Publication statusPublished - 2015
EventChinese Intelligent Automation Conference, 2015 - Fuzhou, China
Duration: 1 Jan 2015 → …

Publication series

NameLecture Notes in Electrical Engineering
Volume336
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

ConferenceChinese Intelligent Automation Conference, 2015
Country/TerritoryChina
CityFuzhou
Period1/01/15 → …

Keywords

  • Machine learning method
  • System modeling
  • Unsteady aerodynamics

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