径流混沌时间序列的模糊支持向量机预测模型研究及其应用  

Study on prediction model of the chaotic runoff time series using fuzzy support vector machines and a case study

作　　者：郭晓亮[1] 王国利[1] 梁国华[1] 王本德[1] 

机构地区：[1]大连理工大学土木水利学院,大连116024

出　　处：《水力发电学报》2010年第3期51-55,共5页Journal of Hydroelectric Engineering

基　　金：国家十一五科技支撑项目(2006BAB14B05);国家自然科学基金项目(50809011)

摘　　要：针对径流混沌时间序列固有的确定性和非线性特点,以传统的支持向量机预测模型为基础,通过引入模糊隶属函数来考虑气候和流域下垫面条件变化,建立了径流混沌时间序列的模糊支持向量机预测模型。通过三种方法对长江寸滩站实际月径流时间序列的预测模拟结果对比,表明本文建立的模型具有更高的模拟精度,是一种有效的径流时间序列预测模型。This paper develops a prediction model of chaotic flow time series by using a fuzzy support vector machine based on traditional SVM and by analyzing the certainty and nonlinearity of these series.This model adopts a fuzzy membership function to quantify the effects of climate changes and the basin conditions.Three models of fuzzy SVM,traditional SVM and neural network are used to manipulate and predict the monthly flow time series at Cuntan of Yangtze River.A comparison shows that the fuzzy SVM is more accurate.

关 键 词：径流时间序列 混沌 支持向量机 模糊隶属度 

分 类 号：TV697[水利工程—水利水电工程]