m1440

说明：  Matlab数理统计方面的图书源码聚类分析，希望对您有用(Matlab mathematical statistics, cluster analysis of source books, I hope useful for you)

fundamentals of CBIR..

Synchronous Buck Converter: This switched power supply converts a 30V DC supply into a regulated 15V DC supply. The model can be used to both size the inductance L and smoothing capacitor C, as well as to design the feedback controller. By selecting between continuous, discrete and fixed-point controllers, the impact of discretization and using limited numerical precision can be explored

随机信号处理，包括各种概率计算，各种功率谱计算(Random signal processing, including all probability, a variety of power spectrum)

编写卡尔曼滤波程序实现航迹的滤波估计，并计算出误差，仿真曲线(Kalman filter procedure to prepare implementation of the filtering track estimate and calculate the error, the simulation curve)

本算法是遗传算法用于解决TSP(货郎担)问题(This algorithm is the genetic algorithm is used to solve the TSP (traveling salesman) problems)

Spectrum Analysis teaching aid

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

本Matlab代码是检验一个有向图是否是一个弱距离正则图(The Matlab code is to test a directed graph is a weak distance-regular graph)

统计代码行、注释行、空行、同时统计函数个数和行数，将代码、注释和空行进行风格评分 (Statistical lines of code, comment lines, blank lines, while the number of statistical functions and the number of rows, the code, comments and blank lines carry style ratings)