| 一种基于熵权法的小波去噪复合评价指标 |
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| 引用本文： | 容 静,刘立龙,康昊华,李松青,周 吕.一种基于熵权法的小波去噪复合评价指标[J].大地测量与地球动力学,2018,38(7):689-694. |
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| 作者姓名： | 容 静 刘立龙 康昊华 李松青 周 吕 |
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| 摘 要： | 传统的评价指标在真值未知的情况下不能满足小波去噪质量评价的要求。为此，借助变化率特征重新构建均方根误差变化量和平滑度变化量两个指标，利用熵权法定权将归一化后的两个指标线性组合，所得到的新指标即为复合评价指标。该方法借助指标的变化率随分解层数的增加表现出明显的收敛特性来确定去噪最优分解层数。实验表明，该方法能够在真值未知的情况下准确地指导小波分解，确定去噪最优分解层数，从而达到最优去噪效果。
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| 关 键 词： | 变形监测 小波去噪 评价指标 熵权 GM（1 1）模型 |
The Deformation Prediction of ARMA and PSO-SVM Model Based on Variance Compensation Adaptive Kalman Filter |
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| RONG Jing,LIU Lilong,KANG Haohua,LI Songqing,ZHOU Lü.The Deformation Prediction of ARMA and PSO-SVM Model Based on Variance Compensation Adaptive Kalman Filter[J].Journal of Geodesy and Geodynamics,2018,38(7):689-694. |
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| Authors: | RONG Jing LIU Lilong KANG Haohua LI Songqing ZHOU Lü |
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| Abstract: | According to the non-linearity, volatility characteristics and real-time dynamic data processing of deformation monitoring data, the auto-regressive and moving average model (ARMA) is used to construct the trend, based on the selection of variance compensation adaptive Kalman filter for stochastic disturbance rejection and model error weakening analysis. The error compensation and correction ARMA model is obtained by using particle swarm optimization (PSO) parameter optimization support vector machine (SVM). The method is used to predict the deformation monitoring engineering. The prediction results show that the method can describe the actual deformation of engineering under complex environmental factors and play a certain reference value in forecasting the project. |
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| Keywords: | Kalman filtering ARMA PSO-SVM error compensation deformation prediction |
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