基于混沌优化支持向量机的大坝安全监控预测

首先介绍了基于统计学习理论的一种新的机器学习技术——支持向量机(Support Vector Machine,SVM),并针对目前支持向量机参数选择时人为选择的盲目性,将具有良好优化性能的混沌优化(Chaos Optimi-zation)技术应用到支持向量机惩罚函数和核函数参数的优化,提出了混沌优化支持向量机(Chaos Optimization Support Vector Machine,COSVM)方法.根据丰满大坝1997-2004年的实际监测数据,建立了混沌优化支持向量机大坝安全监控预测模型,进行了与统计回归模型和BP神经网络模型的分析比较,结果表明,COSVM模型具有更高的预测精度,同时在较长时段的预测中,COSVM模型也表现出更好的泛化推广性能.

Research on safety monitoring forecasting model for dam based on chaos optimization support vector machine algorithm

Support vector machine(SVM),a machine learning algorithm based on statistical learning theory is presented firstly.Aiming at the blindness of man made choice of the parameter and kernel function of SVM,a chaos optimization method is applied to select parameters of SVM;and a novel algorithm,chaos optimization support vector machine(COSVM) is put forward.According to the measured field data(1997-2004) of the Fengman Dam,the COSVM-based safety monitoring model is applied to forecast the deformation of the dam.Compared with statistical regression model and BP neural network model,a conclusion is drawn that the COSVM-based model possesses not only higher precision of forecasting,but also better generalization ability in long-interval forecasting.