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这几项M文件提供了扫描仪如何识别电子标签的方法，在许多场合都会涉及到这一类的智能识别。(This document provides a number of M scanner how to identify methods of electronic tag, in many occasions, will relate to this type of intelligent identification.)

MATLAB image RAW format

对图像进行jpeg变换的程序，希望对大家有帮助，主意不是jpeg2000(Jpeg transform the image process, we want to help, ideas are not jpeg2000)

一般统计数据中有很多变量，采用组成分分析法，可以既有效抓住有效信息，又实现降维。本程序采用实例，来说明此方法。(General statistics, there are many variables, sub-components analysis, can only effectively capture useful information, but also to achieve dimensionality reduction. The program uses examples to illustrate this method. )

这是关于智能天线的自适应算法的MATLAB代码(This is the smart antenna on the adaptive algorithm of MATLAB code)

Matlab搭的双馈感应式风力发电系统机侧变流器、网侧变流器的控制模型，此模型中参数已给定，经调试后能运行。(Matlab ride doubly-fed induction wind power system with machine-side converter, the grid-side converter control model, the model parameters have been given, by the debugger to run.)

智能小车是移动机器人的一种，可通过计算机编程来实现其对行驶方向、启停以及速度的控制。要想让智能小车在行驶过程中能成功地避开障碍物，必须对其进行路径规划?，路径规划的任务是为小车规划一条从起始点到目标点的无碰路径。路径规划方法有：BP人工神经网络法（Back Propagation）、机器学习（Reinforcement Learning）、以及模糊控制（Fuzzy　Control）方法等。(There are many ways on autonomous mobile robot obstacle avoidance system designing, which based on fuzzy control of robot obstacle avoidance system is to use the fuzzy logic control system to determine the environmental information around the robot, then plan out a route to the target point with collision-free path. This method has a small footprint, responsive and could be able to cope with relatively complex external environment. In this paper, the main content is fuzzy control theory based obstacle avoidance of mobile robot path planning by research.)

说明：  一阶倒立摆写状态空间方程，在输入量的控制下，显示最终小车图像(The first-order inverted pendulum writes the state space equation and displays the final car image under the control of the input)

MUSIC算法估计性能分析,详细的介绍了MUSIC算法估计性能(It is very useful in array signal process )

说明：  非线性分类，SVM理论为基础，调整参数改变分类（聚类的）边界的松紧程度(Nonlinear classification, SVM theory, adjust the parameters to change the classification (clustering) the degree of elastic boundary)