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

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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.

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Authors and Affiliations

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

    Fei Qingguo (Ph.D. Candidate) & Zhang Lingmi (Professor)

Authors
  1. Fei Qingguo
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  2. Zhang Lingmi
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Correspondence to Fei Qingguo.

Additional information

Supported by: Specialized Research Fund for the Doctoral Program of Higher Education, China (No. 20010227012)

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Qingguo, F., Lingmi, Z. Application of experimental design techniques to structural simulation meta-model building using neural network. Earthq. Eng. Eng. Vib. 3, 293–298 (2004). https://doi.org/10.1007/BF02858243

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  • DOI: https://doi.org/10.1007/BF02858243

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