EE13M1016_A4

随机森林分类代码，，很详细，，含有含多个函数文件(Random forest classification code, very detailed, containing containing multiple functions file)

经典的国外bitshop关于机器学习和模式识别教程，全是英文，内容详细(Classic foreign bitshop tutorial on machine learning and pattern recognition, all in English, detailed)

说明：  matlab读图像和图像信息，函数imread可以从任何Matlab支持的图像文件格式中，以任意位深度读取一幅图像。(matlab read images and image information, the function imread Matlab support from any image file format to read any bit depth of an image.)

图像相似判断matlab用于两个图片的相似度进行比较和分析，一个采用巴氏系数计算法。另外一个rfg越接近1则越相似(matlab similar )

Probabilistic graphical models in matlab. (Probabilistic graphical models in matlab.)

基于MATLAB的GMSK基带解调器的设计与实现,使用SIMULINK进行仿真和应用

刘金坤《先进 PID 控制及其 MATLAB 仿真》书上第二章源程序。供大家参考。(Liu Jinkun " Advanced PID Control and MATLAB simulation," the book' s second chapter source. For your reference.)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

calls all the tests.

说明：  This code is related to the course Digital Communications. It reproduce the error probability as a function of the SNR per bit for 16-QAM. Enjoy it!