变形监测数据预报的动态贝叶斯ELM方法

Dynamic Bayesian ELM method for deformation monitoring data prediction

贝叶斯极限学习机(BELM)具有充分利用数据先验信息,可以自适应估计模型参数的特点。但在样本数量不断增加时,如果每次都对BELM重新训练将会降低计算效率。针对此问题,本文提出一种动态贝叶斯极限学习机(DBELM)方法以应用于变形监测数据实时预报。该方法以BELM训练的模型参数为初值,根据新增样本信息可对初始模型参数进行动态更新,并从理论上推导了相关计算公式。通过对仿真数据和实际变形数据进行详细分析表明:DBELM方法的预报精度要优于BELM、正则化极限学习机(RELM)、极限学习机(ELM)3种方法。特别是在长期持续预报过程中,其预报性能相对于其余3种方法优势明显。这充分表明了所提方法应用于变形监测数据预报领域具有可行性和有效性。

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.