| 摘要: |
| 针对以往预测模型在数据少和噪音干扰下出现预测精度降低的问题,基于分形理论,尝试建立改进的变维分形预测模型,并以小湾工程边坡位移监测数据为例,选取D1、D2曲线作为预测模型的分形参数曲线,计算各曲线的分段分形维数,对位移进行预测,并分别用灰色模型GM(1,1)和BP神经网络进行对比预测.结果证明,这种方法充分利用了分形理论自相似性的特点,抗噪性强,能较好地应用于小数据量监测数据的预测,并且精度较高,有着良好的应用前景. |
| 关键词: 改进变维分形 边坡监测 预测 小数据量 |
| DOI： |
| 分类号:TV698.1 |
| 基金项目:浙江省水利厅科研项目 |
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| Forecasting model of monitoring data of high rock slope based on improved variable dimension fractal theory |
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QIN Peng,QIN Zhi-hai
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QIN Peng,QIN Zhi-hai(Zhejiang Water Conservancy , Hydropower College,Hangzhou 310018,China)
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| Abstract: |
| Aiming at the problem of low precision caused by insufficient data and noise interruption,the paper attempts to set up and improve the forecasting model of variable dimension based on fractal theory.Meanwhile,with the monitoring data of slope displacement from the Xiaowan Project,and Curve D1,D2 as the fractal parameter curves of the forecasting model,it tends to predict the displacement by calculating the sub-fractal dimension,and comparing the forecast result with the gray model GM(1,1) and BP neural netw... |
| Key words: improved variable dimension fractal monitoring of slope forecast insufficient data fractal theory |
