Gabor

关于Matlab模糊控制工具箱比较系统的教程。适合初学者(On the Matlab Fuzzy Control System Toolbox Tutorial comparison. Suitable for beginners)

说明：  系统辨识中的各种辨识算法：如最小二乘法、递推最小二乘法、广义最小二乘、变遗忘因子递推算法等。(System Identification of identification algorithm : if the least-squares method, recursive least squares method, generalized least squares, changing the forgotten factor algorithm.)

一个老外写的文章，很不错，对以后学习核主元分析（KPCA）有帮助！(the paper was written by the foreigner，which is very useful and meaningful.)

document powerfull windows 7

Zip includes files about Solving ODEs using matlab

压缩机的matlab 仿真，燃气轮机的仿真模块(yasuoji simulink)

2-D–3-D Frequency Registration Using a Low-Dose Radiographic System for Knee Motion Estimation

此文件介绍了matlab的基础知识，适合于初学者进行学习，希望对大家有所帮助(This document describes the basics of matlab, suitable for beginners to learn, we want to help)

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.)

胡广书《现代信号处理》，现代信号处理的经典教材，内附书中的源程序(Hu Guang book " Modern signal processing," the classic textbook of modern signal processing, source code included in the book)