mynormex

最短路问题的MATLAB求解，给出图的邻接矩阵输出源点到汇点的最短距离及路线方案，另外根据Floyd算法可得到任意两点的最短距离！(Of MATLAB to solve the shortest path problem is given adjacency matrix output of the source point to the Meeting Point, the shortest distance and route programs, addition, according to Floyd algorithm for the shortest distance between any two points available!)

Generate Data for target tracking

Cross-correlation of several signals

用matlab编写的简单的读图程序，可以将我们常见的一别曲线图片里的曲线上的具体坐标读取出来，适合初学者(Simple interpret blueprints procedures, using matlab specific coordinates on the curve in our common other curve pictures can be read out, suitable for beginners)

MATLAB AR模型三谱计算程序，我们实验室用的，完美运行

0-1整数规划有很广泛的应用背景，比如指派问题，背包问题等等，实际上TSP问题也是一个0-1问题，当然这些问题都是NP问题，对于规模较大的问题用穷举法是没有办法在可接受的时间内求得最优解的，本程序只不过是一个练习，得意之处是用递归法把所有解都排列出来。另：胡运权所著的《运筹学基础及应用（第三版）》第97页的例3，我用本程序求解得到的结果是：最优解是x*=(1,0, 0, 0, 0)，最优值是f(x*)=8，但书求得最优解是x*=(1,0, 1, 0, 0)，最优值是f(x*)=4，是不是书中写错了，请大家验证。以下是源程序，大家可以任意使用无版权问题，另外，如果大家有大规模的0-1规划的问题也希望提供给我，谢谢。变量个数至少是3个(0-1 Integer Programming is a very broad background, such as assignment, bags and so on. actually TSP is a 0-1, of course, these problems are NP, for larger problem with the law is not exhaustive approach in an acceptable time frame to obtain the optimal solution. this procedure is only one practice, farewell tour is the method used recursive all of the solutions to show. Another : Hu Yun-author of "operational research, and application base (third edition)" Article 97 of the three cases, I use this procedure to solve the result is : is the optimal solution x = (1,0, 0, 0, 0), the optimal values of f (x*) = 8, but the book is to find the optimum solution x = (1,0, 1, 0, 0), the optimal values of f (x*) = 4, the book is not a mistake, please certification. Following is the source)

MUSIC方法估计DOA,并求出了估计角(MUSIC method estimates DOA, and finds the estimated angle )

Getting Start LPC 1768 LandTiger

说明：  求解送取货且带时间窗的绿色车辆路径与调度优化问题(Solving the green vehicle routing and scheduling optimization problem with time window)

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 )