Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4631870 | Applied Mathematics and Computation | 2010 | 14 Pages |
Abstract
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.
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Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Rajan Kumar, Ranjan Ganguli, S.N. Omkar,