Rotorcraft parameter estimation using radial basis function neural network

Kumar, Rajan and Ganguli, Ranjan and Omkar, SN (2010) Rotorcraft parameter estimation using radial basis function neural network. In: Applied Mathematics and Computation, 216 (2). pp. 584-597.

PDF roto.pdf - Published Version Restricted to Registered users only Download (1MB) | Request a copy

Abstract

Increased emphasis on rotorcraft performance and perational 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.

Item Type:	Journal Article
Publication:	Applied Mathematics and Computation
Publisher:	Elsevier Science
Additional Information:	Copyright of this article belongs to Elsevier Science.
Keywords:	System identification; Parameter estimation; Neural networks; Radial basis function; Numerical differentiation
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	12 Apr 2010 08:25
Last Modified:	19 Sep 2010 05:59
URI:	http://eprints.iisc.ac.in/id/eprint/27054

Actions (login required)

View Item