SloshingSquareTank2d

％ because we do not truncate and shift the convolved input ％ sequence, the delay of the desired output sequence wrt ％ the convolved input sequence need only be the delay ％ introduced by the ideal weight vector centred at n=5( Because we do not truncate and shift the convolved input sequence, the delay of the desired output sequence wrt the convolved input sequence need only be the delay introduced by the ideal weight vector centred at n = 5)

这是一个关于流形学习的压缩文件，大家可以根据需要下载(this is about isomap.m)

ber_ldpc G(F)non bariny LDPC code

基于OQPSK调制的瑞丽衰落信道的性能分析与仿真(Performance analysis and Simulation of Rayleigh fading channel based on OQPSK modulation)

Book fuzzy and neural network Book using Matlab

MIMO Space-Time Block Coding (STBC): Simulations and Results Luis Miguel Cort´ es-Pe ˜ na CORTES-PENA ECE6604: PERSONAL & MOBILE COMMUNICATIONS PRESENTED TO DR. GORDON ST ¨ UBER, APRIL 2009 1

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 )

系统辨识的matlab仿真，该方法的辨识过程使用了粒子群算法，计算迅速准确。(Matlab simulation of system identification, the identification of the method using particle swarm optimization algorithm to calculate quickly and accurately.)

使用matlab实现辐射强度的叠加图像及均值(it is about 3d image used matlab)

这是之前上传的Extract_test_IMFCC程序里面调用的子函数，作配合使用，请看清楚再下载.(This is one subfunction of Extract_test_IMFCC uploaded before.Since pudn.com cannot upload several functions, they must divided.)