cyclic2

QPSK调制信号的仿真程序，用于MATLAB仿真，功能比较强大，有图像输出(QPSK modulation signal simulation program for the MATLAB simulation, the more powerful features, there is the image output)

The program about sliding mode control: chap1_1ctrl.m chap1_1plant.m chap1_1plot.m chap1_1sim.m ....

这个matlab程序通过标准的hough变换，并指定圆半径的情况下，在二值图像中检测圆。(The matlab program through standard hough transform, and specify the radius, the two values in the detection of circular images.)

采用MUSIC方法的白噪声频率检测仿真 白噪声中单个正弦信号的频率检测与估计 白噪声中多个正弦信号的频率检测与估计 (MUSIC method using white noise white noise simulation of the frequency of detection of a single frequency sinusoidal signal detection and estimation of white noise in the frequency of a number of sinusoidal signal detection and estimation)

MATLAB2维小波变换经典程序 让你知道而为小波变换是什么一回事(MATLAB2-dimensional wavelet transform classical procedures let you know for what it is about wavelet transform)

运用于风力发电系统的直接转矩系统仿真源代码，绝对RUN的起来(Used in wind power generation system of direct torque control system simulation source code, the absolute RUN' s up)

说明：  简介卫星姿态控制系统的模型，并针对该模型分析故障发生的特点，故 障诊断的难点，以及可能发生故障的部位和故障的类型，给出故障检测的方法。(Introduction of satellite attitude control system model and the model for the analysis of the characteristics of faults, fault diagnosis of the difficulties, and possible failure location and fault type, fault detection methods are given.)

This a two dimensional level set code where the interface grows with normal speed equal to one. The code generates a movie showing the interface motion. This code uses a first order Godunov method.

说明：  倒立摆 Lqr控制，，有matlab仿真文件(Lqr inverted pendulum control, with matlab simulation file)

当信噪比较大时，采用广义最小二乘法可能会出现多个局部收敛点，解决这个问题的方法可用多级最小二乘法，一般来说多级最小二乘法包含三级辨识过程。利用输入输出数据，通过多级最小二乘法，可分别求的辅助模型，过程模型和噪声模型的参数估计值。在高噪声的情况下，多级最小二乘法明显优于广义最小二乘法，其收敛点唯一。(When the signal noise ratio (SNR), and by use of the generalized least squares method may appear more local convergence point, the way of solving this problem can be used multistage least squares method, and generally multistage least squares method includes three identification process. Using input/output data, through the multistage least squares, respectively for the auxiliary model, process model and noise model parameter estimation. In the high noise, multistage least squares method is obviously superior to the generalized least squares method, and its convergence point only.)