Flow-calculation

说明：  S函数的BP神经网络PID控制器Simulink仿真(S function of BP neural network PID controller Simulink simulation)

说明：  这是deitle出版的经典教材《c++大学教程》第五版的全部例题的代码(This document include all the example codes of a classical book---<<c++ university course>> 5th edition published by deitle)

真正的《精通matlab 2011a》完全版实验代码下载 (" Proficient matlab 2011a full version experiment Codes)

说明：  四杆桁架结构的有限元分析(Bar2D2Node); 三梁平面框架结构的有限元分析(Beam2D2Node); 基于3节点三角形单元的矩形薄板分析(Triangle2D3Node); 基于4节点四边形单元的矩形薄板分析(Quad2D4Node); 基于4节点四面体单元的空间块体分析(Tetrahedron3D4Node); 基于8节点六面体单元的空间块体分析(Hexahedral3D8Node);(Finite element analysis of four bar truss structure (bar2d2node); Finite element analysis of three beam plane frame structure (beam2d2node); The analysis of thin rectangular plate based on 3-node triangular element (triangle2d3node); The analysis of rectangular thin plate based on 4-node quadrilateral element (quad2d4node); Tetrahedron 3D 4node (Tetrahedron 3d4node) is used to analyze the spatial block; The spatial block analysis based on 8-node hexahedral element (hexahedral 3d8node);)

说明：  一个关于KHA的算法，他是一种在KPCA基础上进一步训练的算法(a 5,500 on the algorithm, he is a KPCA in further training on the basis of the algorithm)

系统辨识基于梯度校正的参数辨识方法，其中包括随机牛顿法，确定性梯度下降法(System identification based on the gradient correction parameter identification methods, including random Newton' s method, deterministic gradient descent)

说明：  变分模态分解程序，包括函数和测试程序，已加中文注释，另有说明文档。(The program of variational mode decomposition, including function and test program, has been annotated in Chinese and provided with other documents.)

performs canny edge detection algorithm using matlab platform

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.)

matlab阵列信号处理工具箱，请大家一起交流(matlab array signal processing toolbox, please exchange with U.S.)