tiaopinfangzhen

图像的统计独立分量分解程序包，可以用于人脸识别，非监督分类，信号提取等领域(Image independent component decomposition of the statistical package, can be used for face recognition, non-supervised classification, the fields of signal extraction)

Embedded Application using Matlab

负责火星的一个低轨道空间站的设计。为简化结构，该空间站由一系列密闭的立方体（airtight cubical modules ACM）组成，这些ACM曾连接在一起。困扰你的一个问题是致命的病菌也许留在火星的上层大气中。因为空间站有时需要在上层大气中飞行，所以急需在ACM的各个接触面进行额外的防护，无论是边对边还是面对面，连接是密闭的，所以没有细菌能够进入。一个ACM与另外一个ACM共享的面不需要防护。如果在每个ACM每个面上都加防护，花费巨大。因此，需要知道ACM额外防护的确切面数目(Responsible for the design of a low-orbit space station on Mars. In order to simplify the structure of the space station by a series of closed cubes (airtight cubical modules ACM) composed of these ACM had together. One problem is bothering you deadly germs might stay in the upper atmosphere of Mars. Because the station is sometimes necessary to fly in the upper atmosphere, so urgently needed in all contact surfaces of the ACM additional protection, either face to face or side-to-side, the connection is closed, so no bacteria can enter. An ACM ACM shared with another surface does not need protection. If you have added protection on each side of each ACM, costly. Therefore, the need to know the exact number of additional surface protection ACM)

rbf网络来做预测，过程很适合初学者懂的。很详细，很容易的！(rbf neural network to make the forecast, the process is very suitable for beginners understand. Very detailed, easy! )

CMA盲自适应算法的matlab仿真和该方面的论文。程序可以直接运行。相应的算法可以见书《自适应滤波器原理》电子工业出版社。(CMA blind adaptive algorithm matlab simulation and the area papers. Programs can be run directly. The corresponding algorithm can see the book " Principles of Adaptive Filters," Electronics Industry Publishing House.)

说明：  主成分分析算法，用于建立主成分分析模型，采用累计贡献率的方法(pca )

Documents of Detection of driver fatiuge

UltimateToolBox93代码、文档、例子(UltimateToolBox93)

说明：  matlab优化方面的例子，包括最短路、生成树等常见的例子(matlab optimization examples, including the shortest path, spanning trees and other common examples of)

三电平仿真模型，相对两电平谐波更小，极有参考价值(Tri-level simulation model of two level harmonic smaller, relatively great reference value)