Selection of training samples for model updating using neural networks

C. C. Chang, T. Y.P. Chang, Y. G. Xu, W. M. To

研究成果: Article同行評審

37 引文 斯高帕斯(Scopus)

摘要

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 significantly reduce the number of training samples without affecting too much the accuracy of the neural network prediction.

原文English
頁(從 - 到)867-883
頁數17
期刊Journal of Sound and Vibration
249
發行號5
DOIs
出版狀態Published - 31 1月 2002
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