摘要
针对单一预测模型的不足,提出EEMD分解与粒子群灰色支持向量机(particle swarm optimization grey support vector machine,PSOGSVM)相结合的基坑位移预测模型。以基坑时间序列的混沌性为基础,利用EEMD分解时间序列,采用相空间重构技术构造样本,应用PSOGSVM模型进行基坑预测,并与GM(1,1)、SVM、遗传小波神经网络进行对比。结果表明,该算法预测精度好,具有良好的稳定性,可有效地应用于基坑位移预测。
To overcome the deficiency of the single forecasting model, an EEMD-PSOGSVM prediction model of foundation pit displacement is proposed, based on chaotic time series. The EEMD is adapted to decompose the time series, then phase space reconstruction technique is used to reconstruct the sample. The PSOGSVM model is then applied to predict. A comparative study of some deep foundation pit displacement is made by using the GM （1, 1）, SVM and wavelet neural network optimized by genetic algorithm models, respectively. The results show that the predictive accuracy of this method is better and more stable, and that it can be effectively applied into the prediction of foundation pit displacement.
出处
《大地测量与地球动力学》
CSCD
北大核心
2017年第6期599-603,共5页
Journal of Geodesy and Geodynamics
基金
国家自然科学基金(50604009)
辽宁省"百千万人才工程"项目(20100921099)~~
关键词
深基坑位移
EEMD
PSOGSVM
滤波分解
相空间重构
deep foundation pit displacement
EEMD
PSOGSVM
filter decomposition
phase space reconstruction
