小数据量情境下的滑坡位移非线性变化预测模型

赵明霞; 李庆富

doi:10.3969/j.issn.1003-0972.2017.04.002

小数据量情境下的滑坡位移非线性变化预测模型

平顶山学院数学与统计学院, 河南平顶山 467000

基金项目:

平顶山学院青年科研基金重点项目(20120017)

河南省科技发展计划项目(132400410641)

详细信息

作者简介:
赵明霞(1967-),女,河南平顶山人,副教授,主要从事数学建模及其应用研究.

中图分类号: P642.2
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- 文章访问数: 996
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出版历程
- 收稿日期: 2017-01-06
- 修回日期: 2017-06-23
- 发布日期: 2017-10-09

Prediction Model to the Slope Displacement Nonlinear Changing Under the Small-Data Situation

School of Mathematics and Statistics, Pingdingshan University, Pingdingshan 467000, China

摘要

摘要: 为得到小数据量情境下滑坡位移非线性变化趋势的准确估计,融合广义回归神经网络学习速度快、预测精度高和pGM (1,1)模型减小数据随机性并能增强规律性的建模优势,建立了基于pGM (1,1)模型和广义回归神经网络的滑坡位移组合预测模型.两个工程实例与以往研究结果的对比结果,验证了所建模型可行、有效.
- 广义回归神经网络 /
- pGM (1,1)模型 /
- 滑坡位移预测
Abstract: In order to get an accurate estimate of the landslide displacement nonlinear changing trends under the small-data situation, a model based on the pGM (1,1) and generalized regression neural network was proposed. The new model integrates the advantages of the generalized regression neural network learning speed, high prediction accuracy and the pGM (1,1) model of reducing of the data randomness and enhancing the regularity. Comparing two engineering examples using the new model with the results of the previous studies, it was shown that the new method was feasible and effective.
- generalized regression neural network /
- pGM (1,1) model /
- landslide displacement forecasting

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参考文献(20)

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