摘要
传统灰色GM(1,1)预测模型对非负递增时间序列有较好的预测精度,但对波动幅度较大的时间序列预测精度较低。为了提高预测精度,本文在趋势曲线模型和自记忆理论的基础上,建立趋势曲线-自记忆组合预测模型,将该模型应用于深基坑位移预测中,并与灰色GM(1,1)预测模型和趋势曲线预测模型比较,该模型具有较高的预测精度。
Traditional gray GM（1,1） prediction model for non-negative incremental time series have good prediction accuracy,but the prediction accuracy is not enough for large amplitude fluctuation time series.In order to improve prediction accuracy,the trend curve-self-memory combination prediction model is established based on the trend curve model and the self-memory theory,and the model is applied in the prediction of the amount of displacement of deep foundation pit,and with gray GM（1,1） prediction model and trend curve prediction model comparison,the results show that the model has high prediction accuracy.
出处
《河北工程大学学报（自然科学版）》
CAS
2010年第4期13-17,共5页
Journal of Hebei University of Engineering:Natural Science Edition
关键词
深基坑
趋势曲线
自记忆原理
位移预测
deep foundation pit
trend curve
self-memory theory
displacement prediction
