timeanalysis

generates a biometric template from an iris in an eye image.

说明：  在模式识别应用中，用分支定界法(branch-and-board method)进行特征的选择。该程序在matlab环境下实现了该算法，以从六个特征中选择两个最佳特征为例(In pattern recognition applications, using branch and bound method (branch-and-board method) to carry out the characteristics of choice. The program in the matlab environment to achieve the algorithm to select from the following six characteristics of the best features of the two as an example)

这是matlab的时间预测的代码，非常实用，大家共享啊(This is the time predicted matlab code, very useful, share ah)

MATLAB的 界面设计 关于GUI的相关内容2(MATLAB GUI interface design-related content on the 2)

动态矩阵控制simulink程序 动态矩阵控制simulink程序(DMC simulink program)

运用误差载入法提高抗干扰过程的鲁棒性,应用于GPS抗干扰(Error loading method to improve the use of anti-jamming process robustness)

Grossman气动力，可用于获取Volterra级数一阶核的特征系统辨识的输入输出仿真。(Grossman aerodynamics can be used to obtain a first-order Volterra series features core simulation input and output system identification.)

训练错误率和交叉验证错误率相等，在样本比较大时，这个结果是可以预期的；训练错误率一般低于测试错误率，但是当样本数据比较少时，实验也出现了意外，样本多的那组测试错误率比样本少的训练错误率还要小；在本实验中，同组数据的交叉验证错误率比独立测试错误率高，这个反常现象是因为样本的原因所致，交叉验证的样本小，而独立测试时所用训练样本数目大，因而出现这种情况。分类线上，fisher准则是一条直线，而贝叶斯分类器实际上是一个类似椭圆的封闭曲线；很明显，贝叶斯分类器比fisher分类器要好。(Training error rate and cross- validation error rates are equal, the larger the sample , this result is to be expected training error rate is generally lower than the test error rate , but when comparing the sample data came from the experiment there was an accident, that more samples group test error rate less than the training sample error rate is smaller In this experiment, the same set of data cross-validation error rate than independent test error rate, this anomaly is because the sample of reasons , cross-validation sample is small,And independent testing large number of training samples used , resulting in this situation.Classification online , fisher criterion is a straight line , while the Bayesian classifier is actually a closed curve similar to elliptical It is clear that the Bayesian classifier is better than the fisher classifier .)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

FuzzyTree程序下载，此程序有例程，较为方便。(FuzzyTree program download, it has the example, you must change it for your need)