• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Tsinghua Science and Technology >Samples selection for artificial neural network training in preliminary structural design
【24h】

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

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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：人工神经网络（ANN）被应用于网壳的初步结构设计。通过精心选择的训练样本，为增强网络的泛化能力做出了巨大的努力。采用数论方法（NTM）生成具有低差异的样本，即在域中均匀分散的样本，其中差异是对均匀性的定量测量。基于NTM的样本集的差异是等间距样本的差异的1 / 6–1 / 7。在一个案例研究中，将通过基于NTM的样本训练的网络与通过等距样本训练的网络进行比较，以概括性能。结果表明，前者的人工神经网络的计算精度和稳定性均优于后者。结论是，通过使用均匀分布的训练样本而不是简单地堆积数据，可以有效地提高ANN的泛化灵活性。但是，应该通过NTM而不是等距样本来获得更可靠的均匀性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号