fuel-cell2

说明：  Matlab 高级讲义 本书基于MATLAB 5.2版，提供了使用MATLAB的实践性指导。MATLAB已成为适合多学科、多种工作平台的功能强大、界面友好、语言自然并且开放性强的大型优秀应用软件，同时也已成为国内外高等院校高等数学、数值分析、数字信号处理、自动控制理论以及工程应用等课程的基本教学工具。本书按逻辑编排，自始至终用实例描述；内容完整且每章相对独立；是一本简明的MATLAB参考书，既适用于初学者，也适用于高级MATLAB用户。对MATLAB与FORTRAN、C等语言结合的描述更是所有MATLAB书籍中少有的一大特色。 本书适合作为理工科高等院校研究生、本科生教学用书，也可作为广大科研工程技术人员的自学用书。(Matlab )

画箭头用的一个matlab程序，箭头是二维度的，需要说如箭头大小的控制参量(draw an arrow,only the two dimmentional arow,and the arrow is composed of three lines,two short an one long,the results may not viry beatuiful but is very simple)

这个是对于fir带通滤波器以及iir低通滤波器的一个仿真。程序代码通俗易懂，意义很大。(This is a band-pass filter and the fir iir low-pass filter with a simulation. Program code easy to understand, very meaningful.)

说明：  画出系统的阶跃响应曲线，计算系统的上升时间，调节时间，超调量，稳态误差。。。(drawing the system s step response and comput its adjusting time \stastical error)

水下纯方位目标运动分析的UKF滤波算法在目标跟踪的应用(Kalman filter and extended Kalman filter algorithm to the application in target tracking)

解相关及MMSE和传统检测器比较的仿真程序包括有远近，无远近，有躁，无躁。(decorrelation and MMSE and traditional Detectors comparison simulation program including length, no distance, a rash, without rashness.)

matlab与matcom混合编程实现绘图功能，有兴趣的(matlab and Matcom Matlab graphics, interested)

UKF应用于GPS/IMU组合导航系统的MATLAB代码。采用UKF滤波器对GPS/IMU组合导航系统进行信息融合，利用matlab进行了编程仿真验证，结果有效。(UKF MATLAB code used in the GPS/IMU integrated navigation system)

《MATLAB在数学建模中的应用》（第二版）源代码，论坛已有第一版的代码。本书第二版新增“数据建模”和“蚁群算法”，并对“神经网络”部分有大幅修改，遗传算法也有增加一个案例。同时后面的实例部分也有更新。( MATLAB in Mathematical Modeling Applications (second edition) source code, the first edition of the forum have been code. The second version of the new book Data Modeling and ant colony algorithm , and neural networks section there are substantial changes, genetic algorithm has also increased a case. At the same time there are also examples of the back part of the update.)

说明：  边缘预测提出了一种基于预测的、有一定自适应性的边缘检测方法。介绍了其原理和算法实现过程。通过计算两个相邻像素的灰度值的均值、均方差和梯度来预测下一个像素的灰度值,比较预测值和真实值来判断下一个像素是否是边界点。通过与现有同类算法的比较,证明了该算法的优越性(A predictive and adaptive edge detection method is proposed. The principle and implementation process of the algorithm are introduced. The gray value of the next pixel is predicted by calculating the mean, mean square deviation and gradient of the gray value of the two adjacent pixels. The prediction value and the real value are compared to determine whether the next pixel is a boundary point. The superiority of this algorithm is proved by comparing with the existing similar algorithms.)