dIVISIVE-CLUSTER-

Rosenblatt感知机Matlab实现算法线性分类器的第一个迭代算法是1956年由Frank Rosenblatt提出的. 这个算法被提出后, 受到了很大的关注. 感知器在神经网络发展的历史上占据着特殊的位置: 它是第一个从算法上完整描述的神经网络. 在20世纪60年代和70年代, 受感知器的启发, 工程师, 物理学家以及数学家们纷纷投身于神经网络不同方面的研究. (Rosenblatt perceptron Matlab algorithm linear classifier, an iterative algorithm is proposed by Frank Rosenblatt in 1956. The algorithms have been proposed, there have been a lot of attention. Perceptron in the history of the development of neural network occupies a special Location: It is the first complete description of the algorithm neural network in the 1960s and 1970s, was inspired by the perceptron, engineers, physicists and mathematicians have engaged in different aspects of the neural network .)

Texas A&M大学Lihong Wang教授编写的经典，用蒙特卡罗模型模拟光在组织、复杂介质、溶液中的传播。附带详细说明文件。(Classics Professor Lihong Wang Texas A & M University, prepared using Monte Carlo simulation model of light in the organization, the complexity of the medium, the solution spread. With detailed documentation.)

说明：  古典密码 一共4种古典密码的matlab实现，算是一个很实用的加密基础训练(A total of four kinds of classical classical password password matlab implementation, be a very useful basis for training of encryption)

MATLAB矩阵计算大全 Matlab常用的矩阵计算函数命令 (MATLAB matrix calculation used Daquan Matlab matrix to calculate the function command)

数字图像处理，实现自动全局阈值分割，代码和图片经过matlab调试已通过。(Digital image processing, automatic global thresholding segmentation, and pictures through the matlab code debugging has been passed.)

HDI Guide 310111 usefull doc

基于TKGK实验台的实验数据编码，已经定义好控制的类型(TKGK test-bed based on experimental data encoding, has good control over the definition of the type of)

本程序是对V-BLAST系统及其检测算法的仿真，可采用BPSK,QPSK,16QAM,64QAM调制。检测算法为ML，MMSE,ZF,以及采用迫零的连续干扰消除检测算法。(V-blast ml mmse zf)

边界元算法求解正方形导热问题实例。一个简单的二维导热模型，用BEM算法求解，用于学习边界元编程。(Boundary element method for solving square conduction problem instances. A simple two-dimensional heat conduction model, using BEM algorithm for learning boundary element programming.)

Element Remove Attribute N S extends D O M Test Case Source Code for Andriod.