MATLAB-for-EXCEL

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

it contains all the matlab m-files regrding the basic programs, surf and all the basic m-files

一些小程序，主要包括控制系统分析与模型的建立相关的M文件以及simulink文件。(Small programs, including the establishment of control systems analysis and model M-files and simulink file.)

一种SAR成像的新技术，主要运用S-method方法，并进行了优化。(Adaptive S-Method for SAR-ISAR Imaging)

说明：  主成分分析（PCA）的增量实施。 该算法在线更新每个变换系数矩阵新样本，而无需将所有样本保留在内存中。 在某种意义上，该算法在形式上等效于通常的批处理版本给定一个样本，将变换系数设置在末尾过程是相同的。 实时讨论了应用PCA的意义。(Incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples (a sample set is uploaded together with the examples))

实现神经网络控制的rbf逼近函数，十分经典的一个例子(NNc bijin nonlinear function)

reglage de vitesse moteur à courant

一篇很好的英文文献。导师推荐的，关于可变光照下人脸识别，用matlab仿真(A good English literature. Mentor recommended on face recognition under variable illumination, matlab simulation)

利用TEQC处理的多路径数据绘制卫星空间几何多径彩图。(present a MATLAB program for creating colorized maps of high frequency multipath using TEQC report files of single-epoch data.)

函数Jf用来求所付代价的增加值，只要在matlab中输入Jf=Jf(a)得到的结果就是J了，其中a的值为对应的 值； 函数Rf用来求所收益增加值，只要在matlab中输入Rf=Rf(a)得到的结果就是R了，其中a的值为对应的 值； 如果要修改表格的值的话，就对应的修改函数Jf和Rf中的数组y的值就好了，M的值和N的值以及r的值都可以修改的。 (Used to demand the consideration paid for the increase in value of the function Jf, in matlab enter Jf = Jf (a) the results obtained is J, the value corresponding to the value of a function Rf used to ask or gains added value, as long as Rf = Rf (a) input in matlab result is R, where a value of corresponding value If you want to modify the value of the form, it corresponds to modify the function Jf and Rf array y values ​ ​ like , the values ​ ​ of M and N values, and the value of r can be modified.)