层次分析法确定权重必备 ahp

介绍线性算子的基本概念的文章,这是书里面的一个章节(Foundation of linear operator theory)

插值法： （1）拉格朗日插值 （2）牛顿插值 （3）分段线性插值 （4）Hermite 插值 （5）样条插值(Interpolation method: (1) Lagrange Interpolation (2) Newton interpolation (3) piecewise linear interpolation (4) Hermite interpolation (5) spline interpolation)

几个多目标求极值的Matlab算法，包括模式搜索法、Rosenbrock法、单纯形法、Powell法、最速下降法、共轭梯度法、牛顿法、信赖域法(Matlab algorithm, several multi-objective extremum including pattern search method, Rosenbrock method, simplex method, Powell method, the steepest descent method, conjugate gradient method, Newton' s method, trust region method)

多种曲线拟合方法，包括埃尔米特插值，差商，牛顿法，龙格库塔法( U591A u79CD u66F2 u7EBF u62DF u5408 u65B9 u6CD5 uFF0C u5305 u62EC u57C3 u5C14 u7C93 u7279 u63D2 u503C uFF0C u5DEE u5546 uFF0C u725B u987F u6CD5 uFF0C U9F99 u683C u5E93 u5854 u6CD5)

A tensor product (ranks 1 and 2) matlab app

支持向量基预测的小数据量的预测方法。包括数据拟合，多项式预测等功能()

电商的数据集,可用于做数据分析 平台搭建(The data set of e-commerce can be used to build data analysis platform.)

正交变换源程序，带vc工程。 学习正交变换算法的有力工具。(Orthogonal transformation source code, with vc works. Orthogonal transformation algorithm to learn a powerful tool.)

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)

Fortran 95程序设计【彭国伦】第7章原代码，非常有学习价值！(Fortran 95 programming [Lun Peng-- Chapter 7 of the original code, the very value of learning!)