Particle_Swarm_VRP

Newton Rhapson using matlab

用Parzen窗法或者kn近邻法估计概率密度函数，得出贝叶斯分类器，对测试样本进行测试，比较与参数估计基础上得到的分类器和分类性能的差别.2． 同时采用身高和体重数据作为特征，用Fisher线性判别方法求分类器，将该分类器应用到训练和测试样本，考察训练和测试错误情况。将训练样本和求得的决策边界画到图上，同时把以往用Bayes方法求得的分类器也画到图上，比较结果的异同。3．选择上述或以前实验的任意一种方法，用留一法在训练集上估计错误率，与在测试集上得到的错误率进行比较。(Use Parzen Windows method or the kn neighbor method estimated probability density function, draw the bayes classifier, to test sample to test, and the parameter estimation is based on the classifier and get the difference of classification performance. 2. At the same time the height and weight data as the characteristic, with Fisher identification method for linear classifier, and the application of the classifier to training and test sample, investigation and test training error condition. Will the training sample and utility of decision on the drawing boundary paint, and at the same time before the Bayes method with optimal classifier also paint chart, the result of the comparison of the similarities and differences. 3. Select the above or before any a kind of method of experiment, with a method for training set in the estimation error rates, and on the test set get error rates are compared.)

matlab code to detect the different ECG features

matlab 下的遗传算法源码，可以应用到大部分情况的全局寻优。(By using matlab ，I have provided a genetic algorithm source code, which can be applied to most cases of global optimization.)

matlab采用512个频率点绘制各种窗函数的幅频特性(matlab using 512 frequency points, drawing all kinds of window function of the amplitude-frequency characteristics)

Applied Software Measurements by Jones, this is a very good textbook for software engineering

基于matlab实现sift光流,好用。(Sift matlab realize optical flow based, easy to use.)

主要是应用pso工具箱来优化函数griewank，实现粒子群对函数的测试(Mainly applications pso toolbox to optimize function griewank, and particle swarm function test)

小波包的分层和提取特征，提供给大家参考，在研究小波中。(Layered and extract wavelet packet provided for your reference in the study wavelet.)

分类和拟合，支持向量新算法。最小二乘，但只能用于二维数据(Classification and fitting a new support vector algorithm. Least squares, but only for two-dimensional data)