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This article is the second part of a two paper series exploring the application of two advanced computing techniques: artificial neural networks (ANNs) and genetic algorithms (GAs), to the problem of structural parameter identification for an idealised model of an aircraft wing. In this article, GAs are used to determine an idealised finite element model that is representative of the wing of the Pilatus PC-9/A trainer aircraft. This is achieved through an optimisation process that attempts to match the static and dynamic response of the model to measured aircraft structural responses. A number of approaches were trialed with improvements made to each successive approach in an attempt to find a suitable unique parameter set. Structural parameters were found for a three-element model which has characteristics very similar to those of the PC-9/A wing. A comparison is also provided between the performance of the neural network and GA approaches. [ABSTRACT FROM AUTHOR]

This article is the second part of a two paper series exploring the application of two advanced computing techniques: artificial neural networks (ANNs) and genetic algorithms (GAs), to the problem of structural parameter identification for an idealised model of an aircraft wing. In this article, GAs are used to determine an idealised finite element model that is representative of the wing of the Pilatus PC-9/A trainer aircraft. This is achieved through an optimisation process that attempts to match the static and dynamic response of the model to measured aircraft structural responses. A number of approaches were trialed with improvements made to each successive approach in an attempt to find a suitable unique parameter set. Structural parameters were found for a three-element model which has characteristics very similar to those of the PC-9/A wing. A comparison is also provided between the performance of the neural network and GA approaches. [ABSTRACT FROM AUTHOR]