Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm

被引：38
|
作者：
Meng Yue-bo [1 ,2 ]
Zou Jian-hua [1 ]
Gan Xu-sheng [3 ]
Zhao Liang [3 ]
机构：
[1] Xi An Jiao Tong Univ, Syst Engn Inst, Xian 710049, Shaanxi, Peoples R China
[2] Xian Univ Architecture & Technol, Informat & Control Engn Sch, Xian 710055, Shaanxi, Peoples R China
[3] AF Engn Univ, Coll Engn, Xian 710038, Shaanxi, Peoples R China
来源：
NEUROCOMPUTING | 2012年 / 83卷
关键词：
Wavelet; Neural network; Particle swarm optimization; Aerodynamic modeling; Flight data; PARTICLE SWARM OPTIMIZATION; WAVELET NEURAL-NETWORK; IDENTIFICATION;
D O I：
10.1016/j.neucom.2011.12.015
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To depict the aerodynamic characteristics of flight vehicle accurately, a Wavelet Neural Network (WNN) method, based on improved Particle Swarm Optimization (IPSO) algorithm, is proposed for aerodynamic modeling from flight data. First the multi-particle information share strategy and mutation operation are introduced into Simple PSO algorithm in order to improve the modeling capability of WNN, and then according to modeling flow the aerodynamic model from flight data for flight vehicles is established by WNN based on IPSO algorithm. Simulation results show that the method proposed has a good capability with features of precision, convergence and surmounting prematurity or local optimum, and is also effective and feasible for aerodynamic modeling from flight data. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：212 / 221
页数：10
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