MIMO_doa_mvm

说明：  有关于系统呼损率丢包率随新呼叫接入门限的变化规律，用于判定新呼叫的介入指标(Call loss rate on the system packet loss rate with the new call admission threshold variation, used to determine the involvement of the new call indicator)

自适应遗传算法源matlab程序，用到了G-S正交化分解,希望对大家有用。(aAdaptive genetic algorithm source code by Matlab, used in the G-S orthogonal decomposition. )

电力系统最优潮流计算，可以用来确定最优潮流，计算网损等(Optimal power flow calculation can be used to determine the optimal power flow, and other computing net loss)

sampled signal discrete time

用MATLAB回归非线性模型参数,用MATLAB回归非线性模型参数(Linear regression model parameters with MATLAB)

应用KL变换的人脸识别MATLAB实现,人脸识别可以借鉴(K_L decompsition for face recognition)

classification d image avec l algo FCM

我们对离散时间信号进行傅里叶变换和频谱分析。(We do the discrete time signal FFT and spectrum analysis )

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

关于中性点接地的MATLAB仿真模型的搭建，效果还可以(About neutral grounding of MATLAB simulation model construction, the effect can be)