variable-speed-Traffic

说明：  讲述VC++与MATLAB的接口编程，对于初学者有很大的帮助(On the VC++ and MATLAB programming interface, for there is a great help for beginners)

利用参数辨识法提取相关对准参数，从而估计出陀螺漂移和数学平台偏角并进行补偿。仿真结果表明，开路罗经法对准具有较高的对准精度、较好的快速性，能够满足捷联惯导系统在静基座下实现自对准的要求。(Expressions of mathematical platform misalignment angles of the open-loop algorithm are derived and parameter identification method is used to pick-up the corresponding coefficients from angle measurements in order to identify and compensate gyro drifts and platform misalignment．The simulation results show that the open circuit gyrocompass alignment of SINS can all meet the requirement of SINS initial alignment with high accuracy and quick reaction on static base. thus can meet the high-precision alignment requirement of carrier based inertial navigation system on swinging base． )

将指定文件类型进行转换，从而满足MATLAB等不同软件的快速输入要求(To convert the specified file type to meet different MATLAB software for fast input requirements)

计算光子晶体能带结构图的MATLAB源码文件，使用的方法为平面波展开。各物理参数均有注释，光子晶体结构可任意调整。(Calculation of photonic band structure of the MATLAB source code files, the methods used for the plane wave expansion. The physical parameters have comments, photonic crystal structure can be adjusted.)

说明：  matlab与数学建模--源程序 我是初学者，大家可以参考一下(matlab mathematical modeling- source I am a beginner, we can refer to)

FTDI mpsse communication

本人编写的BPAmatlab程序，用于神经网络学习。(I prepared BPAmatlab procedures for neural network learning.)

这是大学毕业时设计的失真度检测仪论文，由模拟陷波器实现的剔除几波，如果改进的话，可以用数字滤波器实现，不过成本更高(It is designed for university graduation thesis distortion detector by analog notch filter removed to achieve a few waves, if improved, you can use digital filters to achieve, but higher costs)

基于BP神经网络的字符识别算法源代码，包括网络仅值训练方法，训练是通 过调用34个字符文件夹，识别率达到99.8 。(BP neural network-based character recognition algorithm source code, including the value of the network only training methods, training is by calling the 34-character file folders, the recognition rate of 99.8 .)

syms x g=exp(x*sin(x)) t=taylor(g,12,2) xd = 1:0.05:3 yd = subs(g,x,xd) ezplot(t, [1,3]) hold on plot(xd, yd, r-. ) title( Taylor approximation vs. actual function ) legend( Taylor , Function )(syms x g = exp (x* sin (x)) t = taylor (g, 12,2) xd = 1:0.05:3 yd = subs (g, x, xd) ezplot (t, [1 , 3]) hold on plot (xd, yd, ' r-.' ) title (' Taylor approximation vs. actual function' ) legend (' Taylor' , ' Function' ))