getfolpd

说明：  一个简单的matlab非线性拟合例子,使用了matlab提供的nlinfit函数.(A simple non-linear fitting matlab example, matlab using the function provided by nlinfit.)

说明：  实现gabor滤波器的滤波功能。里面有3种版本(Achieve gabor filter filtering. There are 3 versions inside)

计算复杂网络平均路径距离，运行无错误，推荐下载！(The computational complexity of network average path distance, error-free operation, it is recommended to download!)

改进节点法建立MNA方程 matlab算法，附有设计文档(Modified nodal equations matlab algorithm established by MNA)

CIC滤波器的仿真程序，可以变换ＣＩＣ滤波器的级联级数及a值．(CIC filter simulation program, so will the CIC filter and a cascading series values.)

数字预失真Wiener模型的matlab实现(Digital predistortion Wiener model matlab implementation)

汽车车牌识别，用matlab编写的简单的程序，很适合新人学习！(license plate recognition)

The recursive function H(T) Calculate the Expected number of failures in (0,T], for Weibull distribution Assumption: T s unit is a day, the smallest time unit.

说明：  FCM聚类算法的MATLAB实现，使用m文件编程实现函数(This is the program about FCM.)

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)