MATLAB6.5-auxiliary-image-processing

用于计算输电网的潮流，充分考虑了线路中的变压器，并且对变压器模型设定了不同的输入方式，较为灵活的输入。(For the calculation of the trend on the transmission grid, give full consideration to the line transformer and the transformer model set a different input methods, more flexible input.)

程序100例各种常用例子 有助于初学者学习(Examples of 100 cases of various common procedures will help beginners to learn)

该工具箱包括了二种分类,二种回归,以及一种一类支持向量机算法 (1) Main_SVC_C.m --- C_SVC二类分类算法 (2) Main_SVC_Nu.m --- Nu_SVC二类分类算法 (3) Main_SVM_One_Class.m --- One-Class支持向量机 (4) Main_SVR_Epsilon.m --- Epsilon_SVR回归算法 (5) Main_SVR_Nu.m --- Nu_SVR回归算法(The kit includes two kinds of classification, two kinds of regression, and a one-class support vector machine algorithm (1) Main_SVC_C.m--- C_SVC II classification algorithm (2) Main_SVC_Nu.m--- Nu_SVC II classification algorithm (3) Main_SVM_One_Class.m--- One-Class Support Vector Machine (4) Main_SVR_Epsilon.m--- Epsilon_SVR regression algorithm (5) Main_SVR_Nu.m--- Nu_SVR regression algorithm)

自适应有限元求解第一类和第二类边界条件的拉普拉斯方程的相关子程序(Adaptive finite element solution of the first and second boundary conditions related to the Laplace equation subroutine)

3-Dimension FDTD代码PEC边界(3-Dimension FDTD code PEC boundary)

说明：  电子书simulation and software radio for mobile communications的程序文件，用于学习MATLAB通信仿真(electronic books and simulation software for mobile radio communications procedures document for learning communications MATLAB simulation)

SVPWM的matlab仿真模型，基于simulink搭建，完美实现两电平逆变输出。附带svpwm算法及实现过程，适合自学研究。(SVPWM matlab simulation model, based on simulink structures, the perfect realization of two-level inverter output. With svpwm algorithm and implementation process, suitable for self-study.)

it is a matlab code for non linear programming.

Compuute the KL divergence

In this report, we devise a methodology for identifying the species of an iris amongst 3 based on 4 distinctive features. We first cover the constitution of the data set and input patterns. We then determine the layout and structure of the neural network we will use for the classification. We continue by testing the network in real life conditions. We conclude with a review of the methods used, and how they could be improved.