高边坡时序位移滚动预测的SVM-Elman模型

基于支持向量机(SVM)和Elman神经网络,提出一种新的高边坡位移时序预测模型——SVM-Elman神经网络预测模型。在对实测数据学习的过程中,寻找最佳学习样本数和最佳测试样本数,利用经粒子群算法优化的SVM模型对边坡位移时间序列进行实时滚动预测;并运用Elman神经网络改进SVM的预测结果,得到SVM-Elman模型预测值,通过比较不同隐含层数的Elman神经网络对预测结果的影响,选择最佳隐含层数的SVM-Elman模型,实现对预测结果的改进。将SVM-Elman模型应用于某混凝土面板堆石坝左岸强卸荷岩体高边坡位移预测分析中,并与传统的SVM预测结果进行比较分析。结果表明,SVM-Elman模型在预测精度上有明显提高,预测结果科学可靠,在岩体高边坡时序位移预测中具有一定的工程应用价值。

A Time Series Prediction Model of High Slope Displacement Based on Support Vector Machine and Elman Neural Network

A new displacement time series predicting model was proposed by integrating support vector machine (SVM) and Elman neural network, named as SVM-Elman model. In the process of measured displacement data learning, by searching the best historical step and the best prediction step, SVM model was optimized by particle swarm algorithm to dynamically forecast the trend of development. In the meantime, Elman neural network has the ability of dynamically reflecting the development trend of the absolute error of SVM model prediction. By comparing the influence of different hidden layers of Elman neural network on the prediction results, the optimal number of hidden layer was determined for SVM-Elman model and hence modifying the predicted data of SVM in real time. The proposed SVM-Elman model was applied to the strong unloading high slope on the left bank of a concrete face rockfill dam, and the prediction result was compared with that of conventional SVM. Results demonstrate that the proposed model has superior accuracy and real application value in predicting the deformations of high slope.