| 基于小波分析与奇异谱分析的RBF神经网络模型预测 |
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| 引用本文： | 崔腾飞,姬现磊,丁鹏文.基于小波分析与奇异谱分析的RBF神经网络模型预测[J].测绘与空间地理信息,2020(6):44-47. |
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| 作者姓名： | 崔腾飞 姬现磊 丁鹏文 |
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| 作者单位： | 山东科技大学测绘科学与工程学院 |
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| 基金项目： | 山东省2017年重点研发项目(2017GSF220010)资助。 |
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| 摘 要： | 小波分析和奇异谱分析都可以作为处理含有噪声数据的方法,这两种去噪方式都可以消除数据中的噪声。基于两种数据处理方法,RBF神经网络预测隧道地表沉降数据的组合预测比单个模型的预测精度高,可获得更好的预测效果,为隧道监测安全提供可靠的参考。
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| 关 键 词： | 小波分析 奇异谱分析 去噪 RBF神经网络 预测分析 |
Prediction of RBF Neural Network Model Based on Wavelet Analysis and Singular Spectrum Analysis |
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| CUI Tengfei,JI Xianlei,DING Pengwen.Prediction of RBF Neural Network Model Based on Wavelet Analysis and Singular Spectrum Analysis[J].Geomatics & Spatial Information Technology,2020(6):44-47. |
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| Authors: | CUI Tengfei JI Xianlei DING Pengwen |
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| Institution: | (College of Geomatics,Shandong University of Science and Technology,Qingdao 266590,China) |
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| Abstract: | Wavelet analysis and singular spectrum analysis are very effective methods for processing time series data.Wavelet analysis and singular spectrum analysis can effectively remove noise and outliers in observation data,and obtain deformation trends and periods.In this paper,wavelet analysis and singular spectrum analysis are applied to tunnel monitoring data processing,and combined with RBF neural network for predictive analysis,and good results are obtained. |
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| Keywords: | wavelet analysis singular spectrum analysis de-noising RBF neural network predictive analysis |
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