compressedsensingmatlab

通信类的哦，希望对大家有用哦,希望大家好好学哦，(very good ,thank you very much ,hehe ,hahaa)

msp430 I2C读写源程序，两个例程。(MSP430 I2C)

本人原创的用遗传算法实现的vrp问题求解，基于matlab语言(I original genetic algorithm is used to achieve the vrp problem solving, based on the matlab language)

将COMTRADE格式的数据转换成为真实的电气量值，并绘制波形(COMTRADE format of the data will be converted into actual electrical value, and draw the waveform)

TSP问题（又名：旅行商问题，货郎担问题）遗传算法通用matlab程序 D是距离矩阵，n为种群个数 参数a是中国31个城市的坐标 C为停止代数，遗传到第 C代时程序停止,C的具体取值视问题的规模和耗费的时间而定 m为适应值归一化淘汰加速指数,最好取为1,2,3,4,不宜太大 alpha为淘汰保护指数,可取为0~1之间任意小数,取1时关闭保护功能,建议取0.8~1.0之间的值 R为最短路径,Rlength为路径长度( TSP problem (also known as: traveling salesman problem, traveling salesman problem) genetic algorithm generic matlab program D is the distance matrix, n is the population number of parameters a C coordinates of the 31 cities to stop algebra, genetic to C on behalf of the program stops, the specific values ​ ​ of C depending on the size and time-consuming, m alpha for the fitness value of the normalized phase-out acceleration index, and is best taken as 1, 2, 3, 4, and not too much eliminated the protection index, it is desirable for any decimal between 0 and 1, taking 1:00 off the protective function, it is recommended to take a value between 0.8 to 1.0 R for the shortest path, Rlength for the path length)

it is the matlab code of particle swarm optimization with a test function that can be change..

bost konverter sebagai penaik tegangan

此压缩包为关于压缩感知的基础程序，有助于初学者进行学习体会，内容较完整。(This archive is on the basis of compressed sensing program that helps beginners learn experience, more complete content.)

Kalman Filtering Theory and Practice Using MATLAB - Grewal and Andrews

function [L,C] = kmeans(X,k) KMEANS Cluster multivariate data using the k-means++ algorithm. [L,C] = kmeans(X,k) produces a 1-by-size(X,2) vector L with one class label per column in X and a size(X,1)-by-k matrix C containing the centers corresponding to each class. Version: 07/08/11 Authors: Laurent Sorber (Laurent.Sorber@cs.kuleuven.be) References: [1] J. B. MacQueen, "Some Methods for Classification and Analysis of MultiVariate Observations", in Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, L. M. L. Cam and J. Neyman, eds., vol. 1, UC Press, 1967, pp. 281-297. [2] D. Arthur and S. Vassilvitskii, "k-means++: The Advantages of Careful Seeding", Technical Report 2006-13, Stanford InfoLab, 2006.