ADRC-with-MATLAB-Simulation

基于天线阵列协方差矩阵的特征分解类DOA估计算法中，MUSIC算法具有普遍的适用性，只要已知天线阵的布阵形式，无论是直线阵还是圆阵，不管阵元是否等间隔分布，都可以得到高分辨的估计结果。(DOA estimation algorithm, MUSIC algorithm has universal applicability of decomposition classes based on the characteristics of the antenna array covariance matrix, known lineup in the form of the antenna array, linear array and circular array, regardless of the elements whether such as interval distribution, estimation result of a high resolution can be obtained.)

将wav文件从十进制转换成二进制，再从二进制转换回去(tansform a wav file from decimal to binary and reverse the process)

用matlab写的单模光纤的LP01模场分布仿真(LP01 mode field distribution simulation matlab write single-mode fiber)

气象通量处理程序，里面有坐标旋转,能量谱分析等代码(Meteorological flux handler coordinate rotation energy spectrum analysis code)

Intel e752x Memory Controller kernel module.

matlab中解决一般线性方程组 微分方程组等各种方程类型的解决方法 入门级的好帮手(matlab help in solving various types of equations of general solutions of differential equations of linear equations, such as the entry-level)

说明：  用最优化方法中的狗腿法求解信赖域子问题的算法编程实现(Algorithm realization of solving trust region subproblem with dogleg method)

基于matlab开发环境，求曲线的峰峰值，以及峰峰值所对应的时刻。(Based on the development environment of matlab, get fengfeng value for the curve, and the moment corresponding to the fengfeng value. moment. )

电力系统直流潮流计算，matlab实现IEEE10机系统的直流潮流计算(DC power system load flow calculation, matlab realize IEEE10 machine system DC power flow calculation)

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.)