摘要
提出了一种基于小波分析与改进支持向量机的大坝位移预测模型。大坝位移的原始监测信号的一维向量经小波去噪、分解后,依次对分解各层次建立SVM预测模型,并基于量子遗传算法对SVM参数寻优,小波重构模型结果,求得大坝位移预测结果。实例分析表明,该方法较传统SVM方法,预测结果更精确。
A dam displacement prediction model based on wavelet analysis and improved support vector machine is proposed in this paper.The one-dimensional vector of the original monitoring signal of dam displacement is de-noised and decomposed by wavelet.Then SVM prediction model is established for each level of decomposition in turn based on the optimization of SVM parameters by quantum genetic algorithm and the results of wavelet reconstruction model,and the prediction results of dam displacement are obtained.The example analysis shows that this method is more accurate than the traditional SVM method.
作者
郭英嘉
谢帮华
张阳
牛景太
陈辉
GUO Yingjia;XIE Banghua;ZHANG Yang;NIU Jingtai;CHEN Hui(School of Hydraulic and Ecological Engineering,Nanchang Institute of Technology,Nanchang 330099,China;National and Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin,Nanchang Institute of Technology,Nanchang 330099,China)
出处
《南昌工程学院学报》
CAS
2019年第6期98-101,共4页
Journal of Nanchang Institute of Technology
基金
江西省科技计划项目(20133BBE50044)
江西省教育厅科学技术研究项目(GJJ180951)
关键词
小波分析
支持向量机
量子遗传算法
位移
预测
wavelet analysis
support vector machine
quantum genetic algorithm
displacement
prediction
