mRMR2

说明：  在MATLAB环境下实现Huffman编码(In the MATLAB environment to achieve Huffman coding)

混沌时间序列预测工具箱，包括了李雅普诺夫指数、分形纬、嵌入纬以及神经网络预测(chaotic time series forecasting tool kit, including the Lyapunov exponent, fractal-wai, Wei and embedded neural network prediction)

vc和matlab混合编程，在vc中调用matlab的函数进行计算和处理，本程序中调用了matlab图像处理函数打开图像并进行灰度处理(vc and matlab mixed programming, matlab function calls are calculated and processed in the vc, the program calls the matlab image processing function to open and grayscale image processing)

这里上载的都是DUDA模式识别课程中的各种算法的MATLAB实现，可以参考DUDA的《模式识别》课本，很实用(DUDA uploaded here are in pattern recognition program MATLAB implementation of various algorithms, see DUDA of " pattern recognition" of textbooks, very useful)

数据挖掘聚类算法中，K-means的matlab实现(Data mining clustering algorithm, K-means the matlab implementation)

当前压缩感知很热，这是其中稀疏采样、重新构建、图像恢复的Matlab代码(CS Frequency sparse)

Large Graph Analysis in the GMine System

16 qam modulation that apply on sequential with viterbi uncoded

GUIA DE AUTOAPRENDIZAJE PARA VISUAL BASIC

说明：  粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 PSO模拟社会采用了以下三条简单规则对粒子个体进行操作：①飞离最近的个体，以避免碰撞。②飞向目标。③飞向群体的中心。这是粒子群算法的基本概念之一。 粒子群算法其基本思想是受许多鸟类的群体行为进行建模与仿真研究结果的启发(Particle swarm optimization (PSO) is a population based stochastic optimization techniques. Based on evolutionary algorithms compared with other groups, they are initialized to a random solution, iterative search through optimal solution. The difference is: the principle of survival of the fittest evolutionary computation to follow, while PSO simulation community. The potential of each solution expressed as a group of particles, each particle has its own position vector and the velocity vector, and a fitness determined by the objective function. All particles in the search space at a constant speed flight, by following the current search to find the optimal values ​ ​ of the global optimum. PSO simulation community has adopted the following three simple rules for the operation of individual particles: ① recently departed individuals, in order to avoid collisions. ② to the target. ③ fly to center groups. This is one of the basic concepts of particle swarm algorithm. PSO algori)