GettingStarted

集员约束的共轭梯度算法应用于自适应波束形成 Set-membership constrained conjugate gradient adaptive algorithm for beamforming 的详细代码(the code for the paper Set-membership constrained conjugate gradient adaptive algorithm for beamforming )

通过产生两个高斯分布的随机数，组合生成瑞利分布的信号，模拟瑞利信道，然后将一个正弦信号以不同方式通过，比较是否有变化。(By generating two Gaussian distributed random number, combined Rayleigh distribution of signal generation, analog Rayleigh Road, then a sinusoidal signal in different ways by, more if there are changes.)

此m文件时实现MATLAB实现函数，Gabor滤波器的傅里叶变换为谐波和高斯函数各自傅里叶变换的卷积。 (Gabor transform！)

一个最简单的主成分分析的源代码，希望对朋友们有所价值(A simple principal component analysis of the source code, I hope my friends have value)

Solutions for communication networks 3.(Solutions for communication networks 3. Good for students to understand exercises in the book.)

基于矩阵位移法的matlab编程，求刚架的内力和位移(Matlab programming based on the matrix displacement method, find the force and displacement of the rigid frame)

MATlab避障控制算法 需要安装Tomlab工具箱，比matlab自带工具箱更具有优点！(MATlab obstacle avoidance control algorithm will need to install Tomlab toolbox, matlab own toolbox more than the benefits!)

AONOLD置乱，自己选择置乱次数，周期为48次，自己选择是加密还是解密(AONOLD scrambling, scrambling to choose the number of cycles 48 times, their choice is the encryption or decryption)

WAM机械臂阻抗算法源代码，控制关节力矩(WAM arm impedance algorithm source code control joint torques)

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.)