UNSPECIFIED (2000) A neural network approach to predicting airspeed in helicopters. NEURAL COMPUTING & APPLICATIONS, 9 (2). pp. 73-82. ISSN 0941-0643

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Abstract

A helicopter's airspeed and sideslip angle is difficult to measure at speeds below 50 knots. This paper describes the application of Artificial Neural Network (ANN) techniques to the helicopter low air-speed problem. Three ANN methods were applied to the problem: a linear network, a Radial Basis Function (RBF) network, and a Multi-Layer Perceptron (MLP), trained using an implementation of the Levenberg-Marquardt (L-M) algorithm. Internally available measurements, such as control positions and body attitudes and rates, were generated using a realistic simulation model of a Lynx helicopter. These measurements formed the inputs to the ANN methods. The MLP was found to be the superior method. Further testing, including a Taguchi analysis, indicated the validity of the method. It is concluded that ANN techniques present a promising solution to the helicopter low airspeed problem.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Journal or Publication Title:	NEURAL COMPUTING & APPLICATIONS
Publisher:	SPRINGER-VERLAG
ISSN:	0941-0643
Date:	2000
Volume:	9
Number:	2
Number of Pages:	10
Page Range:	pp. 73-82
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/13149

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