HybridParticleSwarmOptimization

复形调优算法，，本算法是改进型算法，它在全局范围内寻找最优点，收敛效果好且快(Complex tuning algorithms, the algorithm is modified algorithm, the scope of its global search for the most advantage of faster convergence and better)

说明：  SVM用于模式识别 整理别人的代码（原来的是英文）所得： kernel.m用于内积矩阵的计算 svcplot.m用于绘图 svm168.m是主程序 testlin.m是采用线形内积函数的支持向量机应用的 文件 testrbf.m是采用RBF内积函数的支持向量机应用 的 文件 每个文件中都有说明。 仿真平台matlab7.0, 仿真全部通过 将所有文件拷贝到work目录下（注意不要直接将上层文件夹直接拷贝到work目录下，若那样操作，必须在matlab的file菜单中的set path中设置相应的路径）。 打开matlab，在命令窗口中输入 testlin或testrbf 即可查看结果。 (SVM pattern recognition for collating other people's code (the original is in English) from : kernel.m plot within the matrix for the calculation svcplot.m for graphics is the main svm168.m testlin.m procedure is used linear plot function within the SVM application documents testrbf.m RBF is using plot function within the SVM applications each file documents were described. Matlab7.0 simulation platform, all through the simulation of all the documents copied to the work directory (not directly to the attention of the upper folder directly copied to w contex directory, as if the operation, Matlab in the file menu on the set path corresponding set the path). Open Matlab, in the command window or imported testlin testrbf can see the results.)

线性化法仿真三个参考节点定位一个定位节点，随机噪声均方差随机分布在[4,10]，多径效应，权重分布在[0.85，1]。(Linearization method simulation of three reference nodes positioning a positioning node, the variance of random noise are randomly distributed in [4,10], multi-path effects, the weight distribution in [0.85,1].)

免疫粒子群算法，这种免疫粒子群优化算法结合了粒子群优化算法具有的全局寻优能力和免疫系统的免疫信息处理机制，并且实现简单，改善了粒子群优化算法摆脱局部极值点的能力。(Immune particle swarm algorithm, this immune particle swarm optimization PSO combines the global optimization ability of the immune system and immune mechanisms of information processing algorithm, and the simple, to improve the PSO' s ability to get rid of the local extreme points .)

利用Van der Pol范德波方程，仿真ECG信号，并计算其功率谱等参数(Fan Debo use Van der Pol equation, simulation ECG signal and calculate the power spectrum and other parameters)

the basic space time block code techniqes

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

根据输入的本原多项式系数、级数、延迟相位产生一个m序列。。(creat a m sequence )

particle filter in matlab

伪随机码的模糊函数及其相关特性，用matlab仿真实现(Ambiguity function of pseudo-random code and its related properties, Simulation using matlab)