@inproceedings{d7034a05d8644fedb6f256f5088ba043,
title = "Unsteady aerodynamics modeling using SVM and artificial neural network",
abstract = "Recently, more and more attention has been drawn by the aircraft{\textquoteright}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.",
keywords = "Machine learning method, System modeling, Unsteady aerodynamics",
author = "Yichao Jiang and Qingjie Zhao and Jihong Zhu",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; Chinese Intelligent Automation Conference, 2015 ; Conference date: 01-01-2015",
year = "2015",
doi = "10.1007/978-3-662-46469-4_62",
language = "English",
isbn = "9783662464687",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "577--585",
editor = "Zhidong Deng and Hongbo Li",
booktitle = "Proceedings of the 2015 Chinese Intelligent Automation Conference - Intelligent Information Processing",
address = "Germany",
}