Voice-communication-

matlab环境，瑞利信道下mimo无线通信系统的仿真，包括原始的单入单出siso,到单入双出sido，到单入多处simo,到mimo系统中垂直分层空时码vblast的误码率仿真曲线。(Matlab environment, Rayleigh Channel mimo wireless communication system simulation, including the original single-input single-output siso, single-and double-sido to multiple single-simo, mimo system of vertical layered space-time codes vblast BER simulation curve.)

说明：  《matlab时频分析技术及其应用》该书中的源代码。matlab源码。(this is a code of the book called matlab time-frequency analesis.)

该m文件是数字信号处理实验课上的实验4：离散傅里叶变换(The m-file is an experimental four experimental course on digital signal processing: the Discrete Fourier Transform)

异步电机间接矢量控制Simulink仿真模型，带有转速外环和Iq、Id两个内环，转速和负载指令阶跃变化，3/2变换为恒幅值变换，不包含弱磁控制，磁链指令为恒值。PWM调制算法采用空间矢量调制（SVPWM）。 (Indirect induction motor vector control Simulink simulation model with speed outer and Iq, Id two inner ring speed and load instructions step change, 3/2 is converted into constant amplitude transformation, is not included weakening control, flux instruction for the constant value. PWM modulation algorithm using space vector modulation (SVPWM).)

说明：  bpp sk

BEMD对图像进行分解，包络构造采用Delaunay三角剖分和三次插值，得到分解的结果。 输入：灰度bmp图像 输出：imf1 imf2 imf3 残差(BEMD image decomposition, the envelope structure using Delaunay triangulation and cubic interpolation, the result of decomposition. Input: greyscale bmp image output: imf1 imf2 imf3 residuals)

说明：  基于MATLAB实现bp神经网络对称模式分类(Bp neural network based on MATLAB to achieve a symmetrical pattern classification)

人工智能模拟退火算法，包含了matlab程序解决优化问题。(Artificial intelligence simulated annealing algorithm includes matlab program to solve optimization problems.)

基于小波变换的数字水印算法matlab代码，阐述了负荷预测的应用研究,是一种双隐层反向传播神经网络。( Based on wavelet transform digital watermarking algorithm matlab code, It describes the application of load forecasting, Is a two hidden layer back propagation neural network.)

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 )