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Application of experimental design techniques to structural simulation meta-model building using neural network

Fei Qingguo (费庆国) and Zhang Lingmi (张令弥)

Institute of Vibration Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Abstract: Neural networks are being used to construct meta-models in numerical simulation of structures. In addition to network structures and training algorithms, training samples also greatly affect the accuracy of neural network models. In this paper, some existing main sampling techniques are evaluated, including techniques based on experimental design theory, random selection, and rotating sampling. First, advantages and disadvantages of each technique are reviewed. Then, seven techniques are used to generate samples for training radial neural networks models for two benchmarks: an antenna model and an aircraft model. Results show that the uniform design, in which the number of samples and mean square error network models are considered, is the best sampling technique for neural network based meta-model building.

Keywords: structure engineering; meta-model; neural network; design of experiments; uniform design

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Copyright 2009 IEM. Journal of Earthquake Engineering and Engineering Vibration. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as described below, without written permission from the Publisher. Copying of articles is not permitted except for personal and internal use, to the extent permitted by national copyright law, or under the terms of a license issued by the National Reproduction Rights Organization of China.