ofdm

matlab经典教程，快速掌握matlab基本用法，利用matlab进行各种数字运算，还可以与vc等语言混合编程(matlab classic tutorials to grasp the basic use of matlab, matlab using a variety of digital computing, and other languages can also be mixed with the vc Programming)

matlab simulink主要介绍了simulink模块的使用，适合初学者学习！(matlab simulink simulink module introduces the use of, suitable for beginners to learn!)

一本在Matlab开发环境下，讲解面向对象的编程书籍。适合从事Matlab编程的朋友们。(a book that is suitable for the friends who want to research object oriented programming using Matlab.)

详细的unscented particle filter 程序(Unscented particle filter detailed procedures)

光学弗朗和费衍射模拟的Matlab源代码(Fraunhofer diffraction optical analog of the Matlab source code)

线性分组码的简单实现, 包括生成矩阵,最小码字,二-十进制转换等功能(Simple linear block code to achieve, including the generator matrix, the smallest code word, two- decimal conversion function)

基于小波变换的噪声环境下的语音识别方法，基于小波变换的噪声环境下的语音识别方法(Speech recognition method based on wavelet transform noise environment speech recognition method based on wavelet transform noise environment)

说明：  本程序采用平面梁单元计算两铰抛物线拱的自由振动特性，希望对学习有限元及程序设计有帮助。(The program uses two hinged planar beam element calculation of the free vibration characteristics of the parabolic arch, the finite element and want to learn programming help.)

先进PID控制MATLAB仿真（刘金琨）源代码(MATLAB simulation of advanced PID control (Liu Jinkun) source code)

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)