Aircraft Accessibility Based on Generalized Regression Neural Network

• Published in: Journal of physics. Conference series Vol. 2569; no. 1; pp. 12059 - 12067
• Main Authors: Fan, Zhonghang, Zhang, Xuhui, Lu, Ying, Hu, Yuchuan, Liu, Xuanlin
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2023
• Subjects: Accessibility; Landing sites; Neural networks; Optimization; Physics; Trajectory planning
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2569/1/012059

Abstract According to the requirements of aircraft accessibility judgment, based on the existing research, this paper proposes two accessibility judgment methods based on the “initial position velocity threshold range” and “expected landing site velocity threshold range”. Firstly, the motion model of the aircraft is established, and the accessibility judgment sample library is established by using the trajectory planning method. Then, the initial and landing velocities are selected as the prediction targets, and the neural network is established to achieve accessibility prediction and judgment. Finally, the simulation analysis of the unpowered aircraft diving stage scene is carried out. The results show that the accessibility judgment model designed in this paper has high prediction accuracy and preliminary accessibility judgment ability. This paper realizes the optimization of the existing scheme and provides a method for the research field of accessibility judgment.