𝔖 Bobbio Scriptorium
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SELECTION OF TRAINING SAMPLES FOR MODEL UPDATING USING NEURAL NETWORKS

✍ Scribed by C.C. CHANG; T.Y.P. CHANG; Y.G. XU; W.M. TO

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
362 KB
Volume
249
Category
Article
ISSN
0022-460X

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✦ Synopsis

One unique feature of neural networks is that they have to be trained to function. In developing an iterative neural network technique for model updating of structures, it has been shown that the number of training samples required increases exponentially as the number of parameters to be updated increases. Training the neural network using these samples becomes a time-consuming task. In this study, we investigate the use of orthogonal arrays for the sample selection. A comparison between this orthogonal arrays method and four other methods is illustrated by two numerical examples. One is the update of the felxural rigidities of a simply supported beam and the other is the update of the material properties and the boundary conditions of a circular plate. The results indicate that the orthogonal arrays method can signi"cantly reduce the number of training samples without a!ecting too much the accuracy of the neural network prediction.

2002 Academic Press

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