fde12

DG placement for reduction of power loss in power distribution system using GSA

this is matlab program for showing the result of quasi ostbc

说明：  通过一维映射和二维映射的软判决解码，以减少误码率(the soft-decision of 1D-SD and 2D-SD)

四元数乘法、求逆、共轭、求范数函数，并附有求解矢量旋转坐标的程序例子(Quaternion multiplication, inverse, conjugate, seeking norm function, together with procedures for solving the example of vector rotating coordinate)

OFDM with noisy channel with qpsk

包括最后计算压缩图像的峰值信噪比和压缩效果的源码,利用自然梯度算法，基于K均值的PSO聚类算法。( Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Use of natural gradient algorithm, K-means clustering algorithm based on the PSO.)

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.)

混沌工具箱2及其相关参考文献，可以很方便计算与混沌相关内容。(Chaos Toolbox 2 and its related references, can be easily calculated and chaos related to the content.)

matlab的矩阵的所有的操作函数的使用。每个测试都通过的。(matlab matrix of all the operating functions of use. Each test is passed.)

遗传算法是计算机科学人工智能领域中用于解决最优化的一种搜索启发式算法，是进化算法的一种。这种启发式通常用来生成有用的解决方案来优化和搜索问题。进化算法最初是借鉴了进化生物学中的一些现象而发展起来的，这些现象包括遗传、突变、自然选择以及杂交等。(In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.[1] Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.)