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基于主成分分析的BP神经网络在形变预测中的应用

APPLICATION OF BP NEURAL NETWORK BASED ON PRINCIPAL COMPONENT ANALYSIS IN DEFORMATION FORECASTING

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【作者】 陈兴权王解先谷川

【Author】 Chen Xingquan1),Wang Jiexian1,2) and Gu Chuan1)1)Department of Surveying and Geo-Informatics,Tongji University,Shanghai 2000922)Key Laboratory of Modern Engineering Surveying of SBSM,Shanghai 200092

【机构】 同济大学测量与国土信息工程系同济大学测量与国土信息工程系 上海200092上海200092现代工程测量国家测绘局重点实验室上海200092

【摘要】 为提高形变预测的精度,提出将主成分分析与改进的BP神经网络相结合用于形变监测数据处理。通过编程实现该算法,并用实测数据进行验证,结果表明:与其他方法相比,基于主成分分析的改进BP神经网络能取得更好的预测效果。

【Abstract】 In order to improve deformation forecasting precision,application of principal component analysis and improved BP neural network in deformation monitoring is proposed.For the verification if the new method can enhance the precision and reliability of forecasted data,the proposed algorithm is programmed and verified by use of measured data.The results show that compared with other methods,the improved BP neural network based on principal component analysis can achieve better foresting results.

  • 【文献出处】 大地测量与地球动力学 ,Journal of Geodesy and Geodynamics , 编辑部邮箱 ,2008年03期
  • 【分类号】TP183
  • 【被引频次】23
  • 【下载频次】295
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