ModelsimSimulink

说明：  MATLAB 粒子群代码 能寻找到最优的解(Particle Swarm MATLAB code to find the optimal solution)

是计算电力系统潮流计算的很好的而功能软件(Calculation of Power Flow Calculation is very good and functional software)

MATLAB 从入门到精通这本书里的源代码和所有附图(MATLAB from approaches to master this book of source code and all the appended drawings)

电子工业出版社 《数字信号处理》第2版课后题MATLAB 2-40题(Electronic Industry Press Digital Signal Processing 2nd edition title after title MATLAB 2-40)

经典的Extended-Euclid算法，计算GCD。。。(implement Extended-Euclid)

MATLAB Simulation文件，摩托车仿真，具体操作步骤见说明文档(MATLAB Simulation file, motorcycle simulation, specific steps see documentation)

Four versions of the simulation is available. tsim.cpp - Prints visual representation of the simulation tsimd.cpp - Prints Density versus output flow rate and average velocity tsiml.cpp - Prints Lane Change Probabiliy versus Output flow rate and Average velocity

some related work in image processing.

此为模式识别中的Fisher线性判别方法求分类器。进行了男、女错误率和总错误率的统计。 全部程序流程如下： 1、读取FAMALE.TXT文件把身高或体重给数组，并求x1的样本均值m1和内离散度S1； 2、读取MALE.TXT文件把身高或体重给数组，并求x2的样本均值m2和内离散度S2； 3、求最佳变换向量和阀值点； 4、读取Test2.txt文件把对应的身高或体重给数组A并求A的样本数M； 5、把最佳变换向量和阀值点、x代入判别方程g（x）； 6、对本判别的错误率进行统计。(This is the pattern recognition method, to calculate the Fisher linear discriminant classifier .Were male , female and total error rate error rate statistics . All program flow is as follows : 1 , read FAMALE.TXT file to height or weight to the array, and find the sample mean m1 x1 and internal dispersion S1 2 , read MALE.TXT file to height or weight to the array, and find the sample mean m2 and x2 within the dispersion S2 3 , for the best transformation vector and the threshold point 4 , read Test2.txt file to the corresponding height or weight requirements for the array A and A is the number of samples M 5 , the best transformation vector and the threshold point , x substituted into the discriminant equation g (x) 6 , determine the error rate of the statistics . )

说明：  首先确定被评价对象的因素（指标）集和评价（等级）集；再分别确定各个因素的权重及它们的隶属度向量，获得模糊评判矩阵；最后把模糊评判矩阵与因素的权向量进行模糊运算并进行归一化，得到模糊综合评价结果(Firstly, the factor (index) set and the evaluation (grade) set of the evaluated object are determined; then the weight of each factor and their membership degree vectors are determined separately, and the fuzzy evaluation matrix is obtained; finally, the fuzzy evaluation matrix and the weight vector of the factor are fuzzily calculated and normalized, and the result of the fuzzy comprehensive evaluation is obtained.)