NeuralNetwork_BP_Classification

爬山法 光伏 最大功率跟踪模型 基于matlab的爬山法光伏并网逆变器最大功率跟踪模型-Matlab-based mountain climbing photovoltaic maximum power tracking inverter model(Matlab mountain climbing photovoltaic maximum power tracking 基于matlab的爬山法光伏并网逆变器最大功率跟踪模型-Matlab-based mountain climbing photovoltaic maximum power tracking inverter model)

Optimization of Permanent Magnet Synchronous Motor Vector Control System Based on Particle Swarm Optimization

产生供选择的16qam 64qamOFDM信号(Generating a signal for the selected 16qam 64qamOFDM)

EEMD AND EMD故障诊断新的方法，有效提取故障信号(EEMD AND EMD)

matlab编写的常见调制模式(am,fm,bpsk,qpsk,mask,16qam等)自动识别动态链接库(matlab prepared common modulation mode (am, fm, bpsk, qpsk, mask, 16qam etc.) automatically recognizes the dynamic link library)

使用半带滤波器实现多速率信号处理详细MATLAB代码(The use of half band filter to achieve multi rate signal processing, detailed MATLAB code)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)

说明：  飞蛾火焰优化算法、原论文及matlab仿真程序(Moth flame optimization algorithm, original paper and MATLAB simulation program)

说明：  %inverstable inverse_tb = gf(zeros(1, t+1), m); for i=1:t+1, inverse_tb(i) = alpha^(-i+1); end; %chien's search lambda_v = zero; accu_tb=gf(ones(1, t+1), m); for i=1:n, lambda_v=lambda*accu_tb'; accu_tb = accu_tb.*inverse_tb; if(lambda_v==zero) error(1,n-i+1)=1; else error(1,n-i+1)=0; end end found = find(error(1,:)~=0)

This is an example of creating an MSK signal