Nonlinear identification of aircraft gas-turbine dynamics

Identification results for the shaft-speed dynamics of an aircraft gas turbine, under normal operation, are presented. As it has been found that the dynamics vary with the operating point, nonlinear models are employed. Two different approaches are considered: NARX models, and neural network models,...

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Bibliographic Details
Published in:	Neurocomputing (Amsterdam) Vol. 55; no. 3; pp. 551 - 579
Main Authors:	Ruano, A.E., Fleming, P.J., Teixeira, C., Rodrı́guez-Vázquez, K., Fonseca, C.M.
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01-10-2003
Subjects:	Gas-turbine engines Genetic programming Multiobjective optimisation Neural networks Nonlinear system identification Gas-turbine engines Neural networks Multiobjective optimisation Genetic programming Nonlinear system identification
Online Access:	Get full text
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Summary:	Identification results for the shaft-speed dynamics of an aircraft gas turbine, under normal operation, are presented. As it has been found that the dynamics vary with the operating point, nonlinear models are employed. Two different approaches are considered: NARX models, and neural network models, namely multilayer perceptrons, radial basis function networks and B-spline networks. A special attention is given to genetic programming, in a multiobjective fashion, to determine the structure of NARMAX and B-spline models.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0925-2312 1872-8286
DOI:	10.1016/S0925-2312(03)00393-X