Quantizer

matlab 经典算法，有差值，解方程，画图的源程序。(matlab classical algorithm, there is difference, solve equations, drawing of the source.)

天线实频匹配法优化函数的构造，可扩大频带宽度,达到提高增益的效果(the function structure of the real frequency method)

说明：  OFDM系统发射部分matlab仿真源代码(OFDM system simulation matlab source code transmitter)

这是一个免疫克隆算法程序，求解了一个非线性的函数问题的最优值。(This is an immune clone algorithm procedure for solving a nonlinear problem of the optimal value function.)

说明：  leach协议MATLAB的仿真模拟代码 leach协议MATLAB的仿真模拟代码 leach协议MATLAB的仿真模拟代码 leach协议MATLAB的仿真模拟代码(leach the agreement of the simulation of MATLAB code to leach the agreement of the simulation of MATLAB code to leach the agreement of the simulation of MATLAB code to leach the agreement of the simulation of MATLAB code to)

电力系统可靠性评估基本monte carlo matlab程序(Basic power system reliability assessment procedures monte carlo matlab)

说明：  单相光伏并网系统，逆变器采用dq下的PI控制。包含SIMULINK 2012-2018所有版本(For single-phase photovoltaic grid connected system, PI control under DQ is adopted for inverter. Include all versions of Simulink 2012-2018)

小波分析概念及应用，包括小波变换基础和各种类型的小波变换(wavelet transform)

灰度共生矩阵提取特征值，最近邻算法，进行纹理图像分类。creat_apprentissage用来训练样本，cooccurence是灰度共生矩阵提取特征值，knn是进行k最近邻算法，classif是纹理图像分类(coocurence matrix，using K nearest neighbor to classify textures images.)

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 )