hangjifenxi

说明：  用MATLAB实现最小生成树，可以应用在网络分簇、计算权值等多种应用场合(Using MATLAB to achieve the minimum spanning tree, can be applied to network clustering, calculation of weight and many other applications)

说明：  基于matlab的AM F-msk MDPSK QPSK的仿真。(Based on matlab' s AM F-msk MDPSK QPSK simulation.)

说明：  运用粒子群算法优化支持向量机的回归型预测实例(An example of regression prediction)

code to get optical flow using sum of square difference method

Navier_Stokes_simulation 是NS方程的数值模拟很好的实例。对于N_S方程很感兴趣的人可以借鉴参考(Navier_Stokes_simulation NS equation numerical simulation is a good example. For those who are interested in N_S equation can draw reference)

数值分析实验报告!包含多个实验！ 实验一 非线性方程求根 实验二 线性代数方程组的解法 --------列主元Gauss消元法 实验三 线性代数方程组的解法 ——Gauss-Seidel迭代法等(Numerical Analysis of the experimental report! Contains more than one experiment! Experiment 1 Experiment 2 roots of nonlinear equations of linear algebraic equations the solution out PCA Gauss elimination method experiment trilinear solution of algebraic equations- Gauss-Seidel iteration method, such as)

this program do qam modulation and upsampling and downsampling and use raised cosine filteroj

简单函数优化的遗传算法程序简单函数优化的遗传算法程序简单函数优化的遗传算法程序(Simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization process simple function of the genetic algorithm optimization procedure)

haar二维小波分解，输出四个分解系数子矩阵.将系数重构，得到最终的输出矩阵(haar two dimension wavelet decompositon)

1. 按照式计算相似性矩阵 2. 排除自身的相似度 3. 按照式计算归一化矩阵 4. 按照式计算归一化拉普拉斯图矩阵L 5. 计算L的特征向量，将前个特征值最大的向量按列放置成一个矩阵，即依次为前个特征值最大的特征向量 6. 归一化X形成矩阵Y 7. 对矩阵Y按每行为一个数据点，进行k-means聚类，第行所属的类就是原来所属的类。(1. similarity matrix based on formula computation 2. rule out your own similarity. 3. normalized matrix based on formula computation 4. normalized formula Laplasse matrix L according to formula. 5. Calculate the eigenvectors of L. Place the vectors with the largest eigenvalues in columns into a matrix, that is, the eigenvectors with the largest eigenvalues in turn. 6. normalized X formation matrix Y 7. K-means clustering is applied to matrix Y according to one data point per behavior. The first row belongs to the original class.)