A neural network approach to predicting airspeed in helicopters
UNSPECIFIED. (2000) A neural network approach to predicting airspeed in helicopters. NEURAL COMPUTING & APPLICATIONS, 9 (2). pp. 73-82. ISSN 0941-0643Full text not available from this repository.
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|
|Number of Pages:||10|
|Page Range:||pp. 73-82|
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