资源列表
libsvm_X64CouldUse
- windows 64位下编译好的libsvm，可以直接使用 svmpredict.mexw64和svmtrain.mexw64文件-windows 64 bit The following compiled libsvm, can be used directly svmpredict.mexw64 and svmtrain.mexw64 file
LSMI
- 最小二乘互信息估计法,基于最小二乘法来直接估计互信息。-least-square MI estimator
MutualInfo
- 用非参数估计的方法（核密度估计）来估计互信息-Nonparametric estimation method (kernel density estimation) to estimate the mutual information
PIDalgorithm
- 使用c完成的PID控制算法，实现对温度的有效控制-PID control algorithm using C, realize the effective control of temperature
ant-colony-algorithm
- 蚁群算法的最短路径搜索，能够在栅格法的图形中自动找到最优路径-Shortest Path ant colony algorithm searches
BP
- bp神经网络本构，我们通过bp神经网络处理实验数据，来拟合本构关系曲线。-bp neural network constitutive, we deal with the experimental data by bp neural network to fit the constitutive relation curve.
Electricity-Price-Forecasting
- 简单的用神经网络实现电价曲线的拟合，实现电价的预测-Simple realization price curve fitting using neural network to achieve price forecast
BPNN.py
- BP全称为Back Propagation，意思为反向传播，该方法是用来对人工神经网络进行优化的，即误差反向传播算法。-BP full name is Propagation Back, mean back propagation, the method is used to optimize the artificial neural network, that is, the error back propagation algorithm.
SACR_iMAP
- 针对压缩感知中的off-grid问题的稀疏自校正算法，参考文献“Sparse Frequency diverse MIMO radar imaging for Off-Grid target based on adaptive iterative MAP”-A novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general o
GRNN_PNN
- 将训练集与测试集数据进行归一化； 建立GRNN或PNN神经网络； 利用建立好的神经网络对测试集中的26个乳腺组织样本的类型进行预测； 计算预测正确率（不必计算每类的正确率，只需计算正常或者病变两类的正确率，即只要预测结果与真实值属于同一大类，则认为是正确，否则认为预测错误）-The training set and test data set is normalized Establish GRNN or PNN neural network The use of wel
particle-swarm-optimization
- 利用粒子群优化算法寻找下列多元函数的最大值：f(x, y) = x*cos(2*pi*y) + y*sin (2*pi*x) -2≤x≤2，-2≤y≤2 要求输出最优解、最优解对应的x和y值，以及粒子群优化算法迭代过程中的适应度函数进 化曲线。-Maximum use of particle swarm optimization algorithm to find the following multivariate function: f (x, y) = x*
RBFluntan
- RBF神经网络，用于实现多输入多输出状况仿真-RBF artificial neuron networks