difffilter

基于Matlab环境编写的一些神经网络PID控制和模糊PID控制源代码，其中包含BP pid，CMAC PID,RBF PID，BP数值逼近算法，BP预测控制以及模糊PID。(Matlab-based environment for the preparation of a number of neural network PID control and fuzzy PID control of the source code, which includes BP pid, CMAC PID, RBF PID, BP Numerical approximation algorithm, BP predictive control and fuzzy PID.)

说明：  解线性方程组的迭代法-Jacobi迭代和Gauss-seidel迭代法(Solving linear equations iterative methods-Jacobi iteration and Gauss-seidel iterative method)

说明：  编写程序：基类base，有一个私有数据成员n，派生类Derived公有继承基类base，Derived中新增加一个数据成员m(Programming: the base class base, there is a private data member n, the derived class Derived inherits the base class public base, Derived add a new data member m)

logical operation in matlab for image manipulationg

Canonical Discriminant Analysis is a dimension-reduction technique related to PCA and CCA

LU decomposition to get upper and lover triangle you can you use LU decomposition technique

说明：  自己写的一个基于肤色模型的人脸检测的matlab代码。( I write a face detection based on skin color model with matlab.)

Femtocell. Wireless communication introduction

说明：  matlab,编写的LVQ1数据挖掘分类算法。 (Matlab, prepared by the Data Mining LVQ1 classification algorithm.)

三维点云法向量估计综述由于获取方便、表示简单、灵活等优势，点云逐渐成为常用的三维模型表示方法之一。法向量作为点云必不可少的属性 之一，其估计方法在点云处理中具有重要的位置。另一方面，由于点云获取过程中不可避免的噪声、误差和遮挡，点云中通常含 有噪声、外点和空洞，并且部分采样模型如CAD模型，也会存在尖锐特征，这些都给法向量估计提出了挑战。对当前已有的点云 法向量估计算法进行综述，分析其原理及关键技术，着重分析它们在处理噪声、外点和尖锐特征等方面的能力并给出比较，最后 为未来研究提供了一些建议(The three-dimensional point cloud normal vector estimation summarized obtain convenient, represents a simple, flexible, and becoming one of the methods commonly used three-dimensional model represents the point cloud. Normal vector of one of the as point clouds essential attributes, its estimation method has important position in the point cloud processing. On the other hand, the noise is inevitable in the process to obtain because of the point cloud, error and occlusion, point cloud typically contains noise outside point and empty, and part of the sampling model such as CAD models, there will still be sharp features, which gave the normal vector estimated that presents a challenge. Overview of current existing point cloud normal vector estimation algorithm, analysis of the principle and key technologies, and analyzed their ability to deal with the noise, the outer points and sharp features and gives comparison, and finally provide some suggestions for future research)