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首页> 外文期刊>清华大学学报（英文版） >Samples Selection for Artificial Neural Network Training in Preliminary Structural Design
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Samples Selection for Artificial Neural Network Training in Preliminary Structural Design

机译：初步神经网络设计训练中的人工神经网络训练样本选择

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摘要

An artificial neural network (ANN) is applied in the preliminary structural design of reticulated shells. Major efforts are made to enhance the generalization ability of networks through well-selected training samples. Number-theoretic methods (NTMs) are adopted to generate samples with low discrepancy, i.e., uniformly scattered in the domain, where discrepancy is a quantitative measurement of the uniformity. The discrepancy of the NTM-based sample set is 1/6-1/7 that of samples with equal spacing. In a case study, networks trained by NTM-based samples are compared with those trained by equal-spaced samples in generalizing performance. The results show that both the computational precision and stability of the former ANNs are more satisfactory than those of the latter. It is concluded that the flexibility of ANNs in generalizing can be effectively increased by use of uniformly distributed training samples rather than simply piling data. More reliable uniformity should be obtained, however, through NTMs instead of equal-spaced samples.
机译：一种人工神经网络（ANN）应用于网状壳的初步结构设计。通过精选培训样本来提高网络泛化能力的重大努力。采用数量定理方法（NTMS）来产生具有低差异的样品，即，在域中均匀地散射，其中差异是均匀性的定量测量。基于NTM的样品组的差异为1/6-1 / 7，样品具有相同的间距。在一个案例研究中，将基于NTM的样品训练的网络与概括性性能的相等间隔样品训练的网络进行了比较。结果表明，前Anns的计算精度和稳定性比后者更令人满意。结论是，通过使用均匀分布的训练样本而不是简单的打桩数据，可以有效地增加ANN的灵活性。然而，应通过NTMS而不是相等间隔的样品获得更可靠的均匀性。

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