suanlichengx

it is good for start in speaker recognition.

opnet的培训课件，三天的培训内容，包括了概念介绍，建模，调试，以及两个实例。(opnet training courseware, training days, including the introduction, modeling, testing, and two examples.)

一个简单的电梯模型，使用matlab软件编写的程序仿真出电梯模型(A simple elevator model, the use of simulation matlab software programs written out of the elevator model)

三维ising模型适合没有结构文件需要自己根据实际机构编写(ising model)

Simplex Program made on Matlab

关于matlab中的众多函数，及相对应的作用介绍，便于查找和记忆(With regard to the many functions in matlab, and the corresponding role of the introduction, easy to find and remember)

ga 算法 量子优化计算方法 详细代码(ga Algeria liaangzi good method )

2DPSK的链路仿真，信号源的产生，PCM编码，调制解调以及加燥 (2 dpsk link simulation signal source, PCM coding and modulation demodulation and dry)

本MATLAB程序是基于离散粒子群算法的可中断负荷的优化计算(The MATLAB program is a discrete particle swarm optimization algorithm based on interruptible load calculation)

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 )