Rotorcraft parameter estimation using radial basis function neural network

被引：21
|
作者：
Kumar, Rajan [1 ]
Ganguli, Ranjan [1 ]
Omkar, S. N. [1 ]
机构：
[1] Indian Inst Sci, Dept Aerosp Engn, Bangalore 560012, Karnataka, India
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2010年 / 216卷 / 02期
关键词：
System identification; Parameter estimation; Neural networks; Radial basis function; Numerical differentiation; TIME-VARYING DELAYS; IDENTIFICATION; STABILITY;
D O I：
10.1016/j.amc.2010.01.081
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Increased emphasis on rotorcraft performance and operational capabilities has resulted in accurate computation of aerodynamic stability and control parameters. System identification is one such tool in which the model structure and parameters such as aerodynamic stability and control derivatives are derived. In the present work, the rotorcraft aerodynamic parameters are computed using radial basis function neural networks (RBFN) in the presence of both state and measurement noise. The effect of presence of outliers in the data is also considered. RBFN is found to give superior results compared to finite difference derivatives for noisy data. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码：584 / 597
页数：14
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