Dynamic Bayesian ELM method for deformation monitoring data prediction

doi:10.11947/j.AGCS.2019.20180504

Abstract

Abstract: 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.

Key words: deformation monitoring, real-time prediction, extreme learning machine, dynamic Bayesian extreme learning machine, forecasting performance

CLC Number:

P258

FAN Qian, FANG Xuhua, XU Chengquan, YANG Ronghua. Dynamic Bayesian ELM method for deformation monitoring data prediction[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(7): 919-925.

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