ORDAZ HERNANDEZ, Keny; FISCHER, Xavier; BENNIS, Fouad

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ORDAZ HERNANDEZ, Keny
ESTIA INSTITUTE OF TECHNOLOGY

FISCHER, Xavier
ESTIA INSTITUTE OF TECHNOLOGY

BENNIS, Fouad

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Article de revue

Ce document a été publié dans

International Journal of Computational Intelligence. 2007, vol. 4, n° 1, p. 80--87

Résumé en anglais

In a particular case of behavioural model reduction by ANNs, a validity domain shortening has been found. In mechanics, as in other domains, the notion of validity domain allows the engineer to choose a valid model for a particular analysis or simulation. In the study of mechanical behaviour for a cantilever beam (using linear and non-linear models), Multi-Layer Perceptron (MLP) Backpropagation (BP) networks have been applied as model reduction technique. This reduced model is constructed to be more efficient than the non-reduced model. Within a less extended domain, the ANN reduced model estimates correctly the non-linear response, with a lower computational cost. It has been found that the neural network model is not able to approximate the linear behaviour while it does approximate the non-linear behaviour very well. The details of the case are provided with an example of the cantilever beam behaviour modelling.< Réduire

Mots clés en anglais

artificial neural network

validity domain

cantilever beam

non-linear behaviour

model reduction

URI

https://oskar-bordeaux.fr/handle/20.500.12278/183692

Unités de recherche

ESTIA - Recherche