matlab

matlab教程，matlab参考书，学习matlab必备啊！(matlab for study.)

第一个程序提取了图像灰度级为64的灰度共生矩阵，并计算了能量，熵，对比度，相关性，逆差矩这5个参数.第二个程序可以提取彩色图像的LBP纹理特征，可以提取采样点为8、16、24的统一模式（u2）、旋转不变模式（ri）、统一旋转不变模式（riu2）的LBP值。(The first program to extract a grayscale image GLCM 64, and calculate the energy, entropy, contrast, correlation, inverse difference moment these five parameters. The second program can extract LBP texture feature color images, sampling points can be extracted for 8,16,24 unified mode (u2), rotation invariant pattern (ri), unified rotation invariant pattern (riu2) of LBP values.)

用于时频分析算法，预报误差法参数辨识-松弛的思想，迭代自组织数据分析，加入重复控制，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，各种资源分配算法实现，信号维数的估计。( For time-frequency analysis algorithm, Prediction Error Method for Parameter Identification- the idea of relaxation, Iterative self-organizing data analysis, Join repetitive control, PV modules contain, MPPT module, BOOST module, inverter module, Various resource allocation algorithm, Signal dimension estimates.)

past when the sample data was collected, the future predictions can also be expected to be right. Application of machine learning methods to large databases is called data mining. The analogy is that a large volume of earth and raw material is extracted from a mine, which when processed leads to a small amount of very precious material similarly in data mining, a large volume of data is processed to construct a simple model with valuable use, for example

适合初学电力系统者，能够求出系统的雅克比矩阵，便于理解课本内容(Suitable for beginners power system, able to calculate the system Jacobian matrix, easy to understand textbook content)

用Matlab实现三种MatrixPencil算法，分别为MP、BMP、UMP来估计声源信号的角度和时间。(Using Matlab achieve three Matrix Pencil algorithm, respectively, MP, BMP, UMP and time to estimate the angle of the sound source signal.)

在VC++ 6.0环境下调用Matlab引擎进行混合编程，该程序源码展示了Matlab引擎接口的实现。(In VC++ 6.0 environment, call Matlab engine hybrid programming, the program source code shows the Matlab engine interface.)

粒子滤波程序，内有相应的matlab和vc代码(particle filter)

MD1system design using Matlab GUI

对于svm进行可视化，清晰地划分了分类超平面以及各个类别(Visualization for SVM, a clear division of the classification hyperplane and each category)