资源列表
tvnpv
- 用于特征降维，特征融合，相关分析等,包括AHP，因子分析，回归分析，聚类分析，粒子图像分割及匹配均为自行编制的子例程。( For feature reduction, feature fusion, correlation analysis, Including AHP, factor analysis, regression analysis, cluster analysis, Particle image segmentation and matching subroutines them
2337
- 最大似然（ML）准则和最大后验概率（MAP）准则，滤波求和方式实现宽带波束形成，分析了该信号的时域、频域、倒谱，循环谱等。( Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Filtering summation way broadband beamforming, Analysis of the signal time domain, frequency domain, cepstrum, c
fuheyuce
- 基于BP神经网络的电力系统日负荷预测，模型采用24个输入，24个输出，训练次数为10000，训练目标为0.5，学习速率设置为0.1。(Load forecasting based on BP neural network, the model uses 24 inputs, 24 outputs, the number of training is 10000, the training target is 0.5, the learning rate is set to 0.1.)
GameServer8
- rxjh(yulgang) V8.0 gameserver
generic-algorithm--TSP-CPP-
- 遗传算法解决TSP问题 带有界面 C++程序 (generic algorithm TSP C++)
Multi-objective-evolutionary
- NSGA的源程序，是多目标进化算法的智能算法，可用于多目标优化与决策等方面的计算(NSGA source, multi-objective evolutionary algorithm intelligent algorithm can be used to calculate other multi-objective optimization and decision-making)
模糊神经网络 边坡
- 一类模糊神经网络用于边坡分类预测，注释详细，内容丰富(A kind of fuzzy neural network is used for slope classification prediction, with detailed annotations and rich content.)
GAN-master
- 关于各种GAN的代码实列，包括ｃｇａｎ，ｉｎｆｏｇａｎ，ｗｇａｎ等。(Code listings for various GANs, including cgan, infogan, wgan, etc.)
confusion_matrix1
- 只有一个文件，调用函数即可生成混淆矩阵，参数可在文件中更改(Call the function to generate a confusion matrix, parameters can be changed in the file)
短时功率谱密度
- 语音信号是一个非平稳时变的信号，所以用计算稳态信号的方法计算功率谱密度函数没有太大意义，看不出信号的动态变化，"gonglvpu"给出计算短时功率谱密度函数(Speech signal is a non-stationary time-varying signal, so it is not very meaningful to calculate the power spectral density function by calculating the steady-st
tbtained-numerical
- 用数值方法来求解雅克比矩阵的迭代过程,并且有仿真结果(The iterative process of Jacobian matrix is solved by numerical method, and the simulation results are obtained.)
TensorFlow tf.data 导入数据（tf
- tensorflow 数据集的建立方法与实战应用 数据集建立的四种方法 技巧和需要注意的关键点(Tensorflow data set establishment method and practical application Data set four methods Tips and key points to pay attention to)