Linear_Descrimination_Program

说明：  基于SVM的文本无关说话人识别算法研究，本文在最后用Matlab程序实现了一个基于支持向量机的说话人识别系统试验平台。并根据对参试者进行的大量身份测试试验，总结系统的各方面性能和分析存在的问题，为进一步研究提供了方向和宝贵的经验。 (SVM-based text independent speaker recognition algorithm, the paper used in the final implementation of a Matlab program based on support vector machine speaker recognition system test platform. And tested based on the identity of those who carried out a large number of test trials, summed up the system performance and analysis of all aspects of the problems, provide a direction for further research and valuable experience.)

平面波与涡旋波的干涉产干涉条纹，条纹出现位错。(Plane wave and vortex waves produce interference fringes, stripes appear dislocation.)

用于智能天线系统中的移动目标跟踪算法中的另一种MUSIC算法的MATLAB仿真图谱(intelligent antenna system for the mobile target tracking algorithms another MUSIC algorithm MATLAB Simulation B map)

matlab仿真的GPS信号捕获程序，相关及非相关累积算法(matlab simulation of the GPS signal acquisition process, relevant and non-related accumulation algorithm)

基于matlab简单图像处理系统中对图片进行增强对比度的处理(Based on a simple matlab image processing system to enhance the contrast of the picture processing)

二维声子晶体能带结构计算程序，采用的是平面波展开法，并且可以用matlab到出能带结构图(Dimensional phononic crystal band structure calculation program, using the plane wave expansion method, and can be used to matlab energy band structure of FIG.)

说明：  一点遗传算法的matlab程序，本人初学算法，积累不多，这个是转载的，比较清晰易懂，适于初学者(Point genetic algorithm matlab program, I am learning algorithm, the accumulation of small, this is reproduced, more user-friendly, suitable for beginners)

非刚性ICP 三维网格模型的配准计算,使用径向基函数计算局部变形(non rigid ICP)

针对不同的偏移距的多层介质的avo正演算法(Avo for multilayer dielectric different offset the positive algorithm)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)