Model updating of suspended-dome using artificial neural networks

Abstract

Differences between the practical suspended-dome and the corresponding numerical model are inevitable. To reduce the existing discrepancy, model updating of a suspended-dome was investigated using the back-propagation network in the article. The article first proposed a method to increase the prediction precision of back-propagation network: reducing the range of the training data for the back-propagation network according to the previous prediction results continuously. Then, some parameters that can be measured are updated by the corresponding measured values directly, and other parameters that cannot be directly measured are updated by the corresponding prediction values from back-propagation network. The results indicate that the updated model can predict the experimental model perfectly, and back-propagation network is effective and accurate to predict the given parameters that cannot be described by an algorithm. The results also confirm that the proposed method to increase the prediction precision of back-propagation network is valid.