dla

最小二乘一次完成算法及其MATLAB实现范例.(A complete least-squares algorithm and its implementation examples MATLAB.)

双站SAR成像的例子，里面解决了非线性问题(an example of bisar,in order to deal with the nonlinear problem)

自己编程的基于C++的matlab牛顿拉夫逊潮流计算程序，具有一定的通用性，经多算例验证(Own programming based C++ the matlab Newton Raphson power flow calculation program, has some versatility, multi numerical example)

hmm pdf for Hidden Markov Models

主成分分析法，主要用在光谱处理上的，包括可见和近红外光谱，对于其他的一些统计学数据也比较适用，包括原数据和处理后的数据(Principal component analysis (pca), mainly used on spectrum processing, including visible and near infrared spectrum, and is also applicable for other statistical data)

说的是一个关于MATLAB使用的简单介绍的文件,对初学着还是有一顶帮助的(That is a study on the use MATLAB a brief document, on a beginner or a help)

source code for svm using matlab

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.)

基础程序，便于学习matlab，便于初学者掌握(The basis of procedures, to facilitate learning matlab, easy for beginners to master)

说明：  该源码可以实现OFDM系统的比特加载和功率分配算法的仿真(The source code can be bit-loaded OFDM system and Power Allocation Algorithm for Simulation)