Raol, JR (1995) Feed forward neural networks for aerodynamic modelling and sensor failure detection. Aeronautical Society of India, Journal, 47 (4). pp. 193-199. ISSN 0001-9267Full text not available from this repository.
In this paper, applications of feedforward neural networks for aerodynamic modelling and sensor failure detection are studied.These networks are trained using fast back propagation recursive least squares algorithms with forgetting factors. Specifically,three training algorithms are implemented in PC MATLAB. The numerical results are presented using a simulation of aircraft mathematical models.
|Item Type:||Journal Article|
|Uncontrolled Keywords:||Mathematical models;Aeronautics;Neural networks; Aerodynamics;Failure;Algorithms;Sensors;Computer simulation; Networks;Feedforward;Least squares method;Polycarbonates; Aircraft;Training;Aircraft instruments;Failure detection;Aerodynamic coefficients;Sensors;Neural nets|
|Subjects:||AERONAUTICS > Aircraft Design, Testing & Performance|
|Division/Department:||Flight Mechanics and Control Division|
|Depositing User:||M/S ICAST NAL|
|Date Deposited:||02 Jan 2008|
|Last Modified:||24 May 2010 09:55|
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