Dynamic Bayesian ELM method for deformation monitoring data prediction

被引：0

作者：

Fan Q. ^{[1
]}

Fang X. ^{[1
]}

Xu C. ^{[2
]}

Yang R. ^{[3
]}

机构：

[1] College of Civil Engineering, Fuzhou University, Fuzhou

[2] Ocean College, Minjiang University, Fuzhou

[3] College of Civil Engineering, Chongqing University, Chongqing

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2019年 / 48卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Deformation monitoring; Dynamic Bayesian extreme learning machine; Extreme learning machine; Forecasting performance; Real-time prediction;

D O I：

10.11947/j.AGCS.2019.20180504

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bayesian extreme learning machine (BELM) has the characteristics of making full use of the prior information of data and self-adaptive estimation of model parameters. However, when the sample size increases, the computational efficiency will be reduced if BELM training is repeated every time. To solve this problem, a dynamic bayesian extreme learning machine (DBELM) method is proposed for real-time prediction of deformation monitoring data. This method takes BELM training model parameters as initial values. According to the new sample information, the initial model parameters can be updated dynamically, and the relevant calculation formula is deduced theoretically. The detailed analysis of simulation data and actual deformation data show that the prediction accuracy of DBELM method is better than that of BELM, RELM and ELM.Especially in the long term continuous forecast, its forecasting performance has obvious advantages over the other three methods.This fully demonstrates the feasibility and validity of the proposed method in the field of deformation monitoring data prediction. © 2019, Surveying and Mapping Press. All right reserved.

引用

页码：919 / 925

页数：6

共 25 条

[1] Su H., Li X., Yang B., Et al., Wavelet support vector machine-based prediction model of dam deformation, Mechanical Systems and Signal Processing, 110, pp. 412-427, (2018)
[2] Chen H., Zeng Z., Deformation prediction of landslide based on improved back-propagation neural network, Cognitive Computation, 5, 1, pp. 56-62, (2013)
[3] Wang X., Fan Q., Xu C., Et al., Dam deformation prediction based on wavelet transform and support vector machine, Geomatics and Information Science of Wuhan University, 33, 5, (2008)
[4] Liu H., Ren C., Zheng Z., Et al., Study of a gray genetic BP neural network model infault monitoring and a diagnosis system fordam safety, International Journal of Geo-Information, 7, 4, pp. 1-19, (2018)
[5] Wang X., Kang K., Shen C., Study on MPGA-BP of gravity dam deformation prediction, Mathematical Problems in Engineering, 2017, (2017)
[6] Qian C., Li L., Zhou Z., Dam deformation monitoring data analysis method based on GM-ANN model, China Rural Water and Hydropower, 10, (2015)
[7] Xing Y., Chen C., Liu L., Et al., Dam deformation prediction based on improved genetic algorithm and BP neural network, Computer Engineering and Design, 39, 8, (2018)
[8] Ma L., Ma F., Fan Z., Et al., Dam deformation monitoring model based on improved MMA-SVM, China Rural Water and Hydropower, 5, pp. 144-147, (2015)
[9] Huang G., Zhu Q., Siew C.K., Extreme learning machine: theory and applications, Neurocomputing, 70, 1-3, pp. 489-501, (2006)
[10] Lian C., Zeng Z., Yao W., Et al., Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level, Stochastic Environmental Research and Risk Assessment, 28, 8, pp. 1957-1972, (2014)

← 1 2 3 →