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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.)

简单实用的矩阵束测到达时间（TOA），仿真过程包括：频域信道的仿真，矩阵束计算过程。(simple matrix pencil for TOA,including frenquency domain channel simulation and the algrithm of matrix pencil.)

This program includes the Gaussian Quadrature implementation in matlab.

非线性规划解决在道路拥挤收费下的双模式路网用户平衡问题(Nonlinear Programming solve dual-mode network users to balance road congestion pricing under)

说明：  matlab数学手册，比较适合有基础的读者使用，适合数学分析或者数据分析(matlab mathematics manuals, more suitable for readers based on the use of mathematical analysis or data analysis for)

这是功率谱估计MVDR算法的实现，代码能仿真成功(This is the power spectrum estimation in MVDR algorithm, the code can be successful simulation)

蚁群算法的matlab代码，对于数学建模非常有用。(Ant colony algorithm matlab code, very useful for mathematical modeling.)

小型矩量法解决天线散射问题，在matlab环境下运行。(Small antenna scattering method of moments to solve the problem, run the matlab environment.)

optimal power flow using particle swarm optimization

简单的matlab图象修正算法 采用的是sobel函数(Simple matlab edge extraction algorithm using sobel function)