phase-Features-m-files

用MATLAB实现MPEG中的 ZIG-ZAG 扫描，矩阵查表实现，函数形式，很有用。(MPEG with a MATLAB implementation of the ZIG-ZAG scan matrix look-up table implementation, functional form, is useful.)

本程序主要是把相同的类聚在一起，便于后续的分类，方法使用简单方便。(This procedure is the same class together, to facilitate the subsequent classification method easy to use.)

说明：  旋转不变性子空间（ESPRIT）类算法需要对阵列接收数据协方差矩阵进行特征分解。ESPRIT 类算法是利用接收数据协方差矩阵的信号子空间的旋转不变性，它利用各子阵的信号子空间之间的旋转不变性求出信号的入射角信息。(Rotation invariant subspace (ESPRIT) algorithms need to decompose the covariance matrix of array received data. ESPRIT algorithm is based on the rotation invariance of the signal subspace of the received data covariance matrix. It uses the rotation invariance between the signal subspaces of each subarray to obtain the incident angle information of the signal.)

文档中包含算例和它的单纯形法MATLAB程序(Examples of the simplex method program design and calculation )

ECONOMIC LOAD DISPATCH

雷达专业的经典教材，书籍雷达数据处理及应用，非常有用(Radar data processing and Application)

AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.(AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.)

这里几种功率谱估计方法，对它们的性能进行了比较，可供参考！(Here several methods of power spectrum estimation of their performance are compared for the reference!)

用格拉布斯坏值提出理论于剔除统计样本样本中坏值，将好值和坏值分开(used to delete the bad data for statistic based on the Grubs theory)

说明：  :针对遗传算法易陷入局部最优的不足,在标准遗传算法基础上加入了三个新的操作- 复原、重构和录优操作,使改 进后的遗传算法收敛于全局最优,并在此基础上以路边约束、动态避障和路径最短作为适应度函数,提出了动态避障的路径 规划方法。通过实验仿真验证了算法的有效性、准确性和实时性,并与基于以往的遗传算法的路径规划方法进行比较,结果 表明本文提出的方法在产生的路径长度和算法运行时间上都具有更优的性能。(: Genetic algorithm for the lack of easy to fall into local optimum, in the standard genetic algorithm based on the insertion of three new operations- rehabilitation, reconstruction, and excellent operational record, so that the improved genetic algorithm converges to the global optimum, and in this by the roadside on the basis of constraints, dynamic obstacle avoidance and the shortest path as the fitness function is proposed dynamic obstacle avoidance path planning method. Simulation results through experiments the effectiveness of the algorithm, accuracy and real-time, and with previous genetic algorithm based path planning method to compare the results show that the method proposed in the resulting path length and algorithms have a running time better performance.)