sd

usefull commands for matlab introductions and mathematical fuctions

基于全局误差的BP神经网络辨识的MATLAB仿真程序，代码有注释(Based on global error of BP neural network identification MATLAB simulation program, code annotated)

利用matlab模拟光纤光栅传光的特性分析源代码(Analysis of the source code using matlab to simulate the characteristics of the return optical fiber grating)

this code determines and plots a number of parameters for a finite width pivoted pad slider bearing. Dimensionless pressure, power loss, load carrying capacity, coefficient of friction, isothermal temperature rise etc.

说明：   使用二分法对样本集进行剪辑，剪辑法的思想，就是将样本集分成训练集与考试集， 利用训练集样本对考试集的样本进行分类（使用近邻法），如果考试集中某个样本分类错误的话，将这个样本删除。在该函数中，使用最近邻法，只进行一次剪辑（遍历完考试集中的样本以后退出）。还有一种重复剪辑法（适用于样本比较多的情况），把样本随机分为多个样本集，将相邻的两个样本前一个作为考试集，后一个作为训练集，调用二分剪辑。所有的样本子集剪辑完毕以后，在递归调用，直到没有样本被剪辑掉,没得讲，重复剪辑的效果肯定好一些。 (The sample sets using the dichotomy of the clips, editing law idea is to sample set into training set and test set, using the training set samples to the test set of samples (using the nearest neighbor algorithm), if the examination focused on a sample of classification error then delete this sample. In the function, use the nearest neighbor method, only once clips (focused on a sample of traversal finished after the exit exam). There is also a repeat clip method (samples are more applicable to the case), the samples were randomly divided into multiple sample set, the two adjacent samples of the former one as a test set, the latter one as a training set, call the two sub-clips. All the samples were a subset of the clips after they have finished, in the recursive call, until there are no samples were clip out, did not have to say that the effect of repeated clips definitely better.)

这是一个用Matlab-S函数写的DFIG在alpha-beta发电运行程序(This is the DFIG in the alpha-a beta generation to run the program, write a Matlab-S function)

ecg (electro cardio gram) based authetication in matlab.output varified 100 working code.bla bla bla bla bla bla

首先是针对无或单失效数据情形下，通过多层贝叶斯估计法得到失效概率过后，给出了寿命分布参数的加权最小二乘估计。然后如果失效数据不止一个，使用乘积限估计法来得到失效概率的估计值。(Firstly, the weighted least squares estimation of the life distribution parameters is given by the multi-layer Bayesian estimation method after the failure probability is obtained for the case of no or single failure data. Then if the failure data is more than one, the product limit estimation method is used to obtain the estimated value of the failure probability.)

this program for efficient graph based image segmentation

Source code in matlab of neural network.