基于MALAB的VR回归模型的研究设计书与实现123148010.docVIP

基于MATLAB的SVR回归模型的设计与实现 The Design and Implementation of SVR Regression Model Based on MATLAB 摘 要 支持向量机是根据统计学习理论提出的一种新的学习方法，近年来受到了国内外学术界的广泛重视，并已在模式识别和函数估计中得到广泛应用。支持向量机理论的最大特点是由有限的训练集样本得到的小的误差保证对独立的测试集仍保持小的误差。从而通过支持向量机(SVM)理论,可以建立支持向量回归(SVR)预测模型，以解决各种实际问题。 　　SVR算法是模式识别中应用比较广泛的算法模型之一，它是支持向量机在函数逼近和回归估计中的应用。在SVR回归分析中，使用支持向量机可以使回归函数尽量平滑，其泛化能力强。 　　本文论述了支持向量回归的基本原理和思想，介绍了支持向量回归算法以及所用到 的几种常见的核函数（即线性内核、多项式内核、径向基函数内核、高斯内核）。本设计主要实现的功能有：数据集的创建、内核函数的选取、参数的设置、训练集的回归、数据集的保存与打开。通过不同核函数的选取以及相应参数的设置对输入数据集进行回归。此模型主要解决非线性回归模型的预测。通过实验改变各个参数的不同取值对训练集进行回归，并分别统计出支持向量的个数，回归性能，程序运行时间。最后对回归的结果进行分析，得出各参数对回归性能的影响。 关键词：支持向量回归; 训练算法; 核函数; 线性判别 ABSTRACT Support vector machine (SVM) is a new method of study based on statistical learning theory which has attracted extensive attentions by academic circles both at home and abroad in recent years. It has been widely used in pattern recognition and function estimation. The biggest characteristic of support vector machine (SVM) theory is that a small error limited by the training set of sample can ensure the independent test set’s small error. Thus a support vector regression (SVR) forecasting model can be built by support vector machine (SVM) theory and it can solve various practical problems. SVR algorithm model is one of pattern recognition algorithm, which is more widely used in approximation of function and the application of the regression estimate. In the SVR regression analysis, using support vector machine (SVM) can smooth regression function as far as possible. Its generalization ability is strong. This paper discusses the basic principle of support vector regression and introduces support vector regression algorithm and several common kernel functions (the linear kernel, polynomial kernel and radial basis function (RBF) kernel, the Gaussian kernel etc.). This essay successfully makes these functions work: the creation of data sets, the selection of kernel function, parameter settings, return of the training set, the preservation