说明：  支持向量机和BP神经网络都可以用来做非线性回归拟合，但它们的原理是不相同的，支持向量机基于结构风险最小化理论，普遍认为其泛化能力要比神经网络的强。大量仿真证实，支持向量机的泛化能力强于BP网络，而且能避免神经网络的固有缺陷——训练结果不稳定。本源码可以用于线性回归、非线性回归、非线性函数拟合、数据建模、预测、分类等多种应用场合
(Support vector machines and BP neural network can be used for non-linear regression fit, but they is not the same principle, support vector machine based on structural risk minimization theory, generally considered its generalization ability than the neural network of strong . Inherent defect- a large number of simulation confirmed that generalization ability of support vector machine is stronger than the BP network, but also to avoid neural network training results unstable. The source code can be used for linear regression, nonlinear regression, nonlinear function fitting a variety of applications, data modeling, forecasting, classification)