Application of Optimized Fractional Order EGM (1,1) Model in Deformation Monitoring and Forecasting
Abstract In view of the unsatisfactory fitting and prediction accuracy of deformation monitoring data series, we propose a fractional order EGM (1,1) model, optimized by particle swarm optimization, to fit and predict deformation monitoring data. We use particle swarm optimization (PSO) to select the fractional order, which fits the minimum average relative error of EGM (1,1), and the optimal fractional order EGM (1,1) model is constructed. We use typical deformation monitoring data to validate the optimization model. The results show that the optimization model achieves high accuracy in fitting and predicting deformation monitoring data. It shows that the optimization model is feasible and effective in processing deformation monitoring data.
Key words :
fractional order operator
grey model
particle swarm optimization
deformation monitoring
Cite this article:
YUAN Debao,ZHANG Zhenchao,ZHANG Jun et al. Application of Optimized Fractional Order EGM (1,1) Model in Deformation Monitoring and Forecasting
[J]. jgg, 2020, 40(4): 331-335.
YUAN Debao,ZHANG Zhenchao,ZHANG Jun et al. Application of Optimized Fractional Order EGM (1,1) Model in Deformation Monitoring and Forecasting
[J]. jgg, 2020, 40(4): 331-335.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2020/V40/I4/331
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TANG Jun,LI Yinjian,GAO Xin. GNSS Deformation Monitoring Denoising Method Based on CEEMDAN [J]. jgg, 2021, 41(4): 408-413.
[2]
YUAN Zhiming, LI Peihong, LIU Xiaosheng. Study on the Application of Improved PSO-SVM Model Considering Neighbor-Point in the Settlement Prediction of Foundation Pit [J]. jgg, 2021, 41(3): 313-318.
[3]
LU Tieding,XIE Jianxiong. Deformation Monitoring Data De-Noising Method Based on Variational Mode Decomposition Combined with Sample Entropy [J]. jgg, 2021, 41(1): 1-6.
[4]
TAN Jiangtao,WANG Zhangpeng,ZHONG Bo,DING Jian. Application of Self-Adaptive Parameter Selection for Multiquadric Function in GPS Elevation Fitting [J]. jgg, 2020, 40(8): 832-837.
[5]
YUE Cong,WANG Li,WANG Zhiwei,HAN Qingqing,XU Fu. Information Extraction and Noise Suppression of GNSS Landslide Deformation Based on S-Transformation
[J]. jgg, 2020, 40(4): 335-399.
[6]
WANG Zhiwei,WANG Li,HAN Qingqing,XU Fu,YUE Cong. Data Decoding Method of the Displacement Sensor and ItsApplication in Landslide Monitoring
[J]. jgg, 2020, 40(4): 436-440.
[7]
YUAN Debao,ZHANG Jian,ZHAO Chuanwu,DU Shigao,Peng Jinying. GNSS Height Fitting Based on Improved RBF Neural Network [J]. jgg, 2020, 40(3): 221-224.
[8]
GAN Ruo,CHEN Tianwei,ZHENG Xudong,DUAN Qingda,PAN Mei. Research on Denoising of Deformation Monitoring Data by Improved Wavelet Threshold Function [J]. jgg, 2020, 40(1): 17-22.
[9]
WEN Yaxin,DAI Wujiao. A Single Epoch Ambiguity Algorithm for Medium and Long Baseline in Deformation Monitoring [J]. jgg, 2019, 39(9): 942-946.
[10]
LU Lu,FAN Hongdong,ZHENG Meinan,ZOU Hao. Dynamic Settlement Monitoring and Time Series Analysis of Railway in Mining Area Based on TCP-InSAR [J]. jgg, 2019, 39(2): 164-168.
[11]
LUO Gan,LIANG Yueji,HUANG Yibang. Deformation Analysis Based on a Dual-Tree Complex Wavelet Transform Method [J]. jgg, 2018, 38(9): 958-963.
[12]
LI Pengbo,HU Zhigang,ZHOU Renyu,ZHAO Qile. The Multipath Mitigation Method Based on Observation Domain and Its Application in GNSS Real-Time Deformation Monitoring [J]. jgg, 2018, 38(8): 840-845.
[13]
WANG Xu,WANG Chang. A Kind of Wavelet De-Noising Composite Evaluation Index Based on Entropy Method [J]. jgg, 2018, 38(7): 689-694.
[14]
DUAN Hurong,LI Run,CHEN Shenglei,YAN Quanchao. Robust Inversion of RANSAC-PSO Algorithm——A Case Study on Lushan Earthquake [J]. jgg, 2018, 38(5): 454-458.
[15]
RONG Jing,LIU Lilong,GAN Xiangqian, GU Junfeng, ZHOU Lü. High Speed Rail Deformation Prediction Based on Wavelet Analysis and Grey-Support Vector Machine [J]. jgg, 2018, 38(5): 473-476.