suafaliuchengtu

This code is about wavelet estimate.

本课程使用AMOS（距结构分析）软件对结构方程进行简单的介绍和概述。结构方程模型 (SEM) 包括多种统计技术，如路径分析，验证性因子分析，带潜变量的因果关系模型，甚至方差分析和多重线性回归(This course uses the AMOS software (from the structural analysis), a brief introduction and overview of the structural equation. Structural equation modeling (SEM), including a variety of statistical techniques, such as path analysis, confirmatory factor analysis, causal relationship with the latent variable model, and even analysis of variance and multiple linear regression)

模拟机器人路径规划,采用强化学习中的Q学习算法来实现,最后会返回机器人选择路径的坐标位置(code for path searching)

维纳滤波器的函数调用，实现界面化操作，函数实现了简单的模糊与解模糊操作(Wiener filter function call, the realization of the interface of the operation)

这个是9阶的lms自适应均衡器simulink，是教授给的材料，实际上只要修改阶数，能实现n阶lms自适应均衡器。(This is a 9 bands LMS adaptive equalizer simulink, is to teach the material to, in fact, as long as the modified order to realize n-order LMS adaptive equalizer.)

说明：  Matlab 模拟仿真 MIMO 直线基站均匀分布 蒙特卡罗仿真信道容量。(Matlab simulation MIMO linear Monte-Carlo simulation of uniformly distributed base station channel capacity.)

实现坐标点的旋转，先将坐标移到以坐标系原点为圆心的坐标系中。(To achieve the rotation of the coordinate points。)

三相逆变器SVPWM控制，能有效的分析在svpwm控制下模式下，逆变器输出波形的效果(SVPWM control three-phase inverter, which can effectively analyze svpwm control mode, the effect of the inverter output waveform)

双线性插值 双线性插值 双线性插值 双线性插值(bilinear_interpolate)

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 )