servo_control_torobot

PLL FS1860 源程序,调试过是OK的,大家可以参考一下.(PLL FS1860 source, debugging off is OK, and everyone can reference.)

intro to matlab.contains basic matlab commands

程序为ICA的调用函数，输入为观察的信号，输出为解混后的信号(Procedures for the ICA call the function, enter the observed signal, the output signal unmixing)

说明：  光伏发电系统，电流电压输出曲线波形吐血土建(Photovoltaic systems, current and voltage waveform output curve vomiting civil)

基于方向可调滤波器，对已配准图像进行融合。(Fusion the registered images based on Steerable filters with matlab)

利用MATLAB进行米散射计算，计算大气粒子对于太阳不同波长的辐射损失(M scattering calculations carried out using MATLAB to calculate atmospheric particles of different wavelengths for the solar radiation loss)

能提取matlab的figure文件的x,y轴数据 非常好用！ 网上收集的！(Matlab to extract the figure documents x, y-axis data is very easy to use! Online collection!)

通信系统的防真，用matlab编写，ＱＰＳＫ(communication system simulation by matlab,ＱＰＳＫ)

把辨识分成两步进行：第一步：利用相关分析法获得对象的非参数模型（脉冲响应或相关函数）；第二步：利用最小二乘法、辅助变量法或增广最小二乘法等，进一步求的对象的参数模型。如果模型噪声与输入无关，则Cor-ls相关最小二乘法（二步法）可以得到较好的辨识结果。Cor-ls相关最小二乘法（二步法）实质上是先对数据进行一次相关分析，滤除了有色噪声的影响，再利用最小二乘法必然就会改善辨识结果。能适应较宽广的噪声范围，计算量不大，初始值对辨识结果影响较小。但要求输入信号与噪声不相关(The identification is divided into two steps: the first step: using the correlation analysis method for object nonparametric model (impulse response or related function) The second step: using the least squares method, auxiliary variable method or augmented least squares method and so on, further for the parameters of the object model. If the model input noise and independent, the Cor- ls related least squares (two footwork) can get good recognition results. Cor- ls related least squares (two footwork) is essentially the data to a correlation analysis, filter in addition to the influence of colored noise, using least squares inevitable will improve the identification result. To adapt to a wide range of noise and calculation is not big, the initial value to identification results less effect. But for the input signal and noise are not related)

采用二阶盲源分离程序编写的模态参数识别Matlab工具箱。(Toolbox for performing modal dentification using second order blind source separation methods)