fft2melmx

边缘检测边缘检测边缘检测边缘检测边缘检测边缘检测(Edge detection edge detection edge detection edge detection edge detection edge detection)

Matlab Sample code for 35 lessons

遗传算法的电机优化程序，个人专门编写的，对于初学者来说，可以借鉴(function youhua1() 遗传算法 n-- 种群规模 ger-- 迭代次数 pc--- 交叉概率 pm-- 变异概率 v-- 初始种群（规模为n） f-- 目标函数值 fit-- 适应度向量 vx-- 最优适应度值向量 vmfit-- 平均适应度值向量 clear all close all clc 清屏 tic 计时器开始计时 n=20 ger=100 pc=0.9 pm=0.1 初始化参数 生成初始种群 v=init_population(n,44) 得到初始种群，22串长，生成20*22的0-1矩阵 [N,L]=size(v) 得到初始规模行，列 disp(sprintf( Number of generations: d ,ger)) disp(sprintf( Population size: d ,N)) disp(sprintf( Crossover probability: .3f ,pc)) disp(sprintf( Mutation probability: .3f ,pm)) sprintf可以控制输出格式 待优化问题)

2元BPSK调制加高斯信道硬判决统计误码率(2 yuan plus Gaussian channel BPSK modulation hard decision error rate statistics)

非线性动力学系统中，基于改进的C-C方法的相空间重构参数选择(Nonlinear dynamical systems, based on the phase space reconstruction method to improve the CC parameter selection)

qpsk通过信道信噪比估计，包括qpsk，8psk，16psk，16qam(qpsk through the channel SNR estimation)

用matlab做的经典小游戏俄罗斯方块，有助于提高对matlab的认识(Matlab to do with the classic game Tetris, help increase awareness of the matlab)

matlab 3D绘图工具箱 十分实用 安装自己看教程(Matlab 3D graphics toolbox)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

用镜像矩阵求出矩阵的QR分解（正交三角分解），并求解线性方程组，该方法不必选主元，但其计算过程非常稳定。也可用于求矩阵的广义逆和求解线性最小二乘问题。子过程QRDCMP用镜像矩阵求m*n矩阵A的QR分解，A=Q^TR,其中Q是m*m正交矩阵，R为m*n上三角矩阵；子过程QRBKSB用矩阵的QR分解求解线性方程组Ax=b，其中A为n阶非奇异方阵。(Matrix obtained with the mirror matrix QR decomposition (orthogonal triangular decomposition), and solving linear equations, the method does not have to choose the main element, but its calculation process is very stable. Can also be used to find the generalized inverse matrix and solving linear least squares problems. Sub-process of the mirror matrix QRDCMP m* n matrix A QR decomposition, A = Q ^ TR, wherein Q is a m* m orthogonal matrix, R is m* n upper triangular matrix subprocess QRBKSB a QR decomposition to solve the matrix linear equations Ax = b, where A is non-singular square matrix of order n.)