An Adaptive Genetic Wavelet Neural Network and Its Application in Risk Assessment for Controlled Flight into Terrain
Authors
Xusheng Gan, Dengkai Yao, Yarong Wu, Zhe Dai
Corresponding Author
Xusheng Gan
Available Online April 2015.
- DOI
- 10.2991/isrme-15.2015.219How to use a DOI?
- Keywords
- Controlled flight into terrain; Risk Assessment; Genetic algorithm; Wavelet neural network
- Abstract
In order to improve the level of safety management and decision-making of Controlled Flight Into Terrain (CFIT), a CFIT risk assessment model based on an adaptive genetic algorithm and wavelet neural network technology is propose. In the model, wavelet neural network can dynamically adjust the parameters of the network by learning the samples, the adjustment of these parameters can effectively reflect the change of each influence factor, and an adaptive genetic algorithm is introduced to solve the selection problem of initial network parameters. The practical example shows that the proposed model has a better assessment performance for CFIT risk.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xusheng Gan AU - Dengkai Yao AU - Yarong Wu AU - Zhe Dai PY - 2015/04 DA - 2015/04 TI - An Adaptive Genetic Wavelet Neural Network and Its Application in Risk Assessment for Controlled Flight into Terrain BT - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering PB - Atlantis Press SP - 1049 EP - 1052 SN - 1951-6851 UR - https://doi.org/10.2991/isrme-15.2015.219 DO - 10.2991/isrme-15.2015.219 ID - Gan2015/04 ER -