x2f_mpi_pack

基于802.11b协议上的一个无线局域网物理层的模型。利用MATLAB的SIMULINK软件平台来实现其功能。(Agreement based on a 802.11b wireless LAN physical layer model. MATLAB using the SIMULINK software platform to achieve its functions.)

matlab出错信息的技术，可以帮助我们快速找到出错的原因，提高编程效率。(matlab error information technology that can help us quickly find the wrong reasons, to improve programming efficiency.)

说明：  本程序可以完成单级倒立摆反馈控制系统设计。(The Program can realize the inverted pendulum feedback control design)

利用matlab绘制图像灰度直方图，能够表现像素统计的结果(Using matlab to draw the image histogram, the statistical results can show pixels)

MATLAB图形技术—绘图及图形用户接口，本书主要介绍matlab绘图技术，通俗易懂。(MATLAB graphics- drawing and graphical user interface, this book introduces the matlab graphics, easy to understand.)

这个是一个HHT的镜像延拓的emd程序。可以适当地解决emd变换的端点效应问题。你可以下下来参考下。(This is a mirror image of HHT emd Extension program. Emd can transform an appropriate solution to end effects. You can refer to the next down.)

matlab 使用源码 主成分分析相关源吗 好东西大家一起用(matlab code of pca )

用matlab实现神经网络的城市交通流量预测。主要以大脑生理变化过程为基础、模仿大脑的结构和功能(Neural network using matlab implementation of the urban traffic flow prediction. Major physiological changes to brain-based process, to imitate the structure and function of the brain)

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.)

说明：  使用matlab仿真自适应光学系统，包括变形镜，哈特曼传感器等，有详细的使用说明，是英文的(Matlab is used to simulate adaptive optics system, including deformable mirror, Hartmann sensor, etc)