摘要
施工仿真参数对拱坝施工进度仿真结果的准确性具有重要影响。目前施工仿真参数分析多采用单变量建模方式,部分采用多元分析方法建立点预测模型,均难以分析各类因素影响下施工仿真参数的不确定性。针对上述问题,本研究提出基于宽度学习系统-弹性网分位数回归(broad learning system-elastic network quantile regression,BLS-ENQR)的施工仿真参数概率预测方法。宽度学习系统不需要深度网络结构,具有高效的非线性学习能力,可以克服传统分位数回归模型仅能分析线性关系的不足;同时,采用弹性网正则化惩罚来减小回归系数并提高模型稀疏性,从而避免模型过拟合。工程应用表明,本研究提出的方法可以有效分析出施工仿真参数的概率分布,且相比于支持向量机-弹性网分位数回归、极限学习机-弹性网分位数回归、宽度学习系统-分位数回归模型,该方法具有更好的预测性能,为拱坝施工仿真参数预测提供了一种新思路。
Construction simulation parameters are essential to the accuracy of actual arch dam construction schedule implementation.Of the previous studies on analysis of these parameters,most adopted a univariate model and some resorted to the multivariate analysis method to build a point prediction model,both facing a difficulty in evaluation of their uncertainty under the influence of various factors.To analyze the probability distribution of the parameters effectively,a probability prediction method based on a broad learning system along with the elastic network quantile regression(BLS-ENQR)is presented.BLS is a neural network that,without demanding a deep network structure,has an efficient nonlinear learning capability,so that it can overcome the deficiency of the traditional quantile regression model limited to linear relationship analysis.The method uses the elastic network regularization penalty to reduce the regression coefficient and improve its sparsity so as to avoid model overfitting.Engineering application shows that this new method can effectively analyze the probability distribution of construction simulation parameters,and it has prediction performance better than the support vector machine-ENQR(SVM-ENQR),extreme learning machine-ENQR(ELM-ENQR),or broad learning system-QR(BLS-QR).
作者
宋文帅
关涛
任炳昱
王佳俊
SONG Wenshuai;GUAN Tao;REN Bingyu;WANG Jiajun(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China)
出处
《水力发电学报》
CSCD
北大核心
2022年第9期150-160,共11页
Journal of Hydroelectric Engineering
基金
国家自然科学基金(51909187
51879186
51839007)。
关键词
拱坝
施工仿真参数
概率预测
宽度学习系统
分位数回归
arch dam
construction simulation parameters
probability prediction
broad learning system
quantile regression
