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用matlab编程，实现了资源调度算法。(With the matlab programming,it can solve resource scheduling algorithm。)

this ebook is consist of cloud computing basics

变尺度随机共振代码，几年前用过的，喜欢的可以试试。(Stochastic resonance code, you can try a few years ago used.)

LMSE算法实现判断线性可分性和求分界向量(LMSE algorithm for linear separability judgment and seek demarcation vector)

sw sift master matlab

%% 用聚类法确定葡萄酒分级 % 《MATLAB数学建模方法与实践》(《MATLAB在数学建模中的应用》升级版)，北航出版社，卓金武、王鸿钧编著.(Classification of wine by clustering method Mathematical Modeling Methods and Practice in MATLAB (Upgraded Edition of Application of MATLAB in Mathematical Modeling), edited by Zhuo Jinwu and Wang Hongjun, Beihang Publishing House.)

说明：  此程序是用矩阵的方法解决一些常见问题，比如说学生成绩排序筛选等问题，希望对一些人有用。(This program is a matrix methods to solve some common problems, such as student achievement to sort filter and so on, want to be useful for some people.)

空间矢量svpwm控制的matlab仿真模型(Space vector svpwm control matlab simulation model)

matlab命令大全，写程序时查起来方便，希望大家有用！(matlab command Guinness, write to facilitate the search process, I hope everyone useful!)

说明：  它主要研究的是多因变量对多自变量的回归建模，特别当各变量内部高度线性相关时，用偏最小二乘回归法更有效。另外，偏最小二乘回归较好地解决了样本个数少于变量个数等问题。 偏最小二乘法是集主成分分析、典型相关分析和多元线性回归分析3种分析方法的优点于一身。它与主成分分析法都试图提取出反映数据变异的最大信息，但主成分分析法只考虑一个自变量矩阵，而偏最小二乘法还有一个“响应”矩阵，因此具有预测功能。(It is mainly a result of the study is a multi-variable regression modeling and more independent variables, especially when the internal height of each variable linear correlation, partial least squares regression method is more effective. In addition, partial least squares regression solves the number of samples is less than the number of variables and other issues. Partial least squares method is a set of principal component analysis, canonical correlation analysis and multiple linear regression analysis of the advantages of the three methods in one. It is the principal component analysis are trying to extract the maximum information reflects data variability, but the principal component analysis considers only one independent variable matrix, and partial least squares and a &quot response&quot matrix, predictive capabilities.)