Genetic-Algorithms

本书介绍了matlab guide的设计方法, 讲解的非常清楚,包含了大量的实例程序,对于全面了解和掌握matlab 界面设计有很大的参考价值(book on Matlab guide the design method, on the very clear, There are a lot of examples of procedures for a comprehensive understanding and grasp of Matlab interface design is a great reference value)

this are the example codes from control system engineering from NORMAN NISE

上电后,在BRAKE信号无效,且保护逻辑无输出的情况下? 电机按DIRECTION信号的方向转动? 电流传感放大器采样电机电枢电流,经13脚的RC网络转换为电压值,与D/A转换输出的预定阀值相比较。如果当前的电流使转换的电压值小于阀值?(Power after the BRAKE signal is invalid, and the protection logic no circumstances ? electrical output signals according to the direction of rotation DIRECTION ? current sensing amplifiers motor armature current sample by 13 feet of RC network is converted to voltage value, and D/A conversion output compared to the target threshold. If the current of current to voltage conversion value is less than threshold ?)

最新版本libsvm，快点珍藏吧，很有用啊(The latest version of libsvm, quick collection of it, very useful )

傅里叶变换 Z变换 拉普拉斯变换的gui(Fu Liye transform, Z transform, Laplasse transform, GUI)

MATLAB编程实现连续断层工业CT图像的三维重建()

Mammogram Enhancement

计算高斯光束束腰半径调节compute guassian beam (compute guassian beam )

matlab编译后出现讨厌的DOC窗口,本程序可清除DOC窗口(Matlab Compiler after irritating DOC window, the procedure will remove DOC window)

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.)