基于线性规划支持向量回归的混沌系统预测

支持向量机是一种基于统计学习理论的新颖的机器学习方法,该方法已经广泛用于解决分类与回归问题。标准的支持向量机算法需要解一个二次规划问题,当训练样本较多时,其运算速度一般很慢。为了提高运算速度,介绍了一种基于线性规划的支持向量回归算法,并由此提出几种新的回归模型,同时将它们应用到混沌时间序列预测中,并比较了它们的预测性能。在实际应用中,可以根据具体情况灵活地选择所需模型。

Chaotic System Prediction Based on Linear Programming Support Vector Regression

Support Vector Machine(SVM) is a kind of novel machine learning method,based on statistical learning theory,which becomes the hotspot of machine learning because of its excellent learning performance.The method of support vector machine has been developed for solving classification and regression problems.Training a support vector machine requires the solution of a very large quadratic programming problem.The algorithm is usually slow when there are many data.In order to improve the running speed,a support vector regression(SVR) algorithm based on linear programming is introduced,three new regression models are proposed,and then their performances are compared.In practice,model may be flexibly selected in terms of requires.