物理学报Issue(16)：1-11,11.DOI:10.7498/aps.63.160508
用于混沌时间序列预测的组合核函数最小二乘支持向量机
Combination kernel function least squares supp ort vector machine for chaotic time series prediction
摘要
Abstract
Considering the problem that least squares support vector machine prediction model with single kernel function cannot significantly improve the prediction accuracy of chaotic time series, a combination kernel function least squares support vector machine prediction model is proposed. The model uses a polynomial function and radial basis function to construct the kernel function of least squares support vector machine. An improved genetic algorithm with better convergence speed and precision is proposed for parameter optimization of prediction model. The simulation experimental results of Lorenz, Mackey-Glass, Sunspot-Runoff in the Yellow River and chaotic network traffic time series demonstrate the effectiveness and characteristics of the proposed model.关键词
混沌时间序列/最小二乘支持向量机/组合核函数/改进遗传算法Key words
chaotic time series/least squares support vector machine/combination kernel function/improved genetic algorithm引用本文复制引用
田中大,高宪文,石彤..用于混沌时间序列预测的组合核函数最小二乘支持向量机[J].物理学报,2014,(16):1-11,11.基金项目
国家自然科学基金重点项目(批准号:61034005)资助的课题.* Project supported by the Key Program of the National Natural Science Foundation of China (Grant No.61034005) （批准号:61034005）
