Selection of training samples for model updating using neural networks

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

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)867-883
Number of pages17
JournalJournal of Sound and Vibration
Issue number5
Publication statusPublished - 31 Jan 2002
Externally publishedYes


Dive into the research topics of 'Selection of training samples for model updating using neural networks'. Together they form a unique fingerprint.

Cite this