Angle-Analysis

基于MATLAB开发的PID控制程序，含有详细说明温习，初学者可以参考学习。(MATLAB-based development of the PID control program, with detailed study, beginners can refer to learn.)

说明：  语音识别关键性技术的MATLAB仿真实现.pdf(Voice recognition key technologies MATLAB simulation. Pdf)

Apriori算法 matlab 自己在网上找的比较全的代码(Apriori algorithm matlab yourself online to find a comparison of the code)

haze removal (cvpr09) matlab with face beautifulcation algorithmn

this file describes and run the HALFTONING of a picture.

用MATLAB中Simlink工具箱对一类随机中立型时滞系统进行图像仿真.(Simlink toolbox with MATLAB, a class of stochastic neutral delay systems, image simulation.)

马尔科夫链蒙特卡洛方法的一个例子，对初学者有用(An example of Markov chain Monte Carlo method, useful for beginners)

DRAM算法，是一种改进型的蒙特卡罗马尔科夫链（MCMC）采样方法，该算法基于经典的MCMC算法，在此基础上增加了改进策略，是算法能够更快速稳定的收敛。(DRAM algorithm is an improved Mirkov chain (MCMC) sampling method, the algorithm is based on the classic MCMC algorithm, based on the increase in the improvement of this strategy is the algorithm to more rapid and stable convergence.)

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.)

基于随机生成的LDPC码的仿真程序，包括编解码过程(Simulation program based on randomly generated LDPC codes, including the process of encoding and decoding)