灰度图像边缘检测算法的matlab实现

burgers方程 matlab的代码 初始的函数要求是一个周期函数

基于VTk的点云显示，构网（Delaunay Tin，包含二维，三维，及TEN）。附有详细的代码注释，也有实例数据。基于VTk的点云显示，构网（Delaunay Tin，包含二维，三维，及TEN）。附有详细的代码注释，也有实例数据。

用matlab进行离散数据的平面拟合，得到平面拟合方程的系数

关于制作车牌识别系统的基础讲解及程序，很有用。

频率估计的matlab源码，详细的程序内容和使用方法

1) 动态规划法求解问题的一般思路，动态规划法求解本问题的思路及其C/C++程序实现与算法的效率分析。2) 贪心算法在0-1背包问题求解中的应用3) 回溯法求解问题的一般思路，回溯法求解本问题的思路及其C/C++程序实现与算法的效率分析。4) 分支限界法求解问题的一般思路，分支限界法求解本问题的思路及其C/C++程序实现与算法的效率分析。有代码！！

Minix原来是荷兰阿姆斯特丹的Vrije大学计算机科学系的Andrew S. Tanenbaum教授所发展的一个类Unix操作系统。全部的程序码共约12,000行，并置于他的著作Operating Systems: Design and Implementation(ISBN 0-13-637331-3)的附录里作为范例。Minix的系统要求在当时来说非常简单，只要三片磁片就可以启动。　　全套Minix除了起动的部份以汇编语言编写以外，其他大部份都是纯粹用C语言编写。分为：内核、内存管理及档案管理三部份。　　Minix原始是设计给1980年代到1990年代的IBM PC和IBM PC/A

稀疏自编码深度学习的Matlab实现，sparse Auto coding，Matlab codetrain, m/7% CS294A/CS294W Programming Assignment Starter CodeInstructions%%%This file contains code that helps you get started ontheprogramming assignment. You will need to complete thecode in sampleIMAgEsml sparseAutoencoder Cost m and computeNumericalGradientml For the purpose of completing the assignment, you domot need tochange the code in this filecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencodtrain.m∥%%========%6% STEP 0: Here we provide the relevant parameters valuesthat willl allow your sparse autoencoder to get good filters; youdo not need to9 change the parameters belowvisibleSize =8*8; number of input unitshiddensize 25number of hidden unitssparsity Param =0.01; desired average activation ofthe hidden units7 (This was denoted by the greek alpharho, which looks like a lower-case pcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod4/57train.,m∥in the lecture notes)1 ambda=0.0001%o weight decay parameterbeta 3%o weight of sparsity penalty term%%==:79 STEP 1: Implement sampleIMAGESAfter implementing sampleIMAGES, the display_networkcommand shouldfo display a random sample of 200 patches from the datasetpatches sampleIMAgES;display_network(patches(:, randi(size(patches, 2), 204, 1)), 8)%为产生一个204维的列向量,每一维的值为0~10000curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod5/57train.m/v%中的随机数,说明是随机取204个 patch来显示%o Obtain random parameters thetatheta= initializeParameters ( hiddenSize, visibleSize)%%=============三三三三====================================97 STEP 2: Implement sparseAutoencoder CostYou can implement all of the components (squared errorcost, weight decay termsparsity penalty) in the cost function at once, butit may be easier to do%o it step-by-step and run gradient checking (see STEP3 after each stepWecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod6/57train. m vb suggest implementing the sparseAutoencoder Cost functionusing the following steps(a) Implement forward propagation in your neural networland implement the%squared error term of the cost function. Implementbackpropagation tocompute the derivatives. Then (using lambda=beta=(run gradient Checking%to verify that the calculations corresponding tothe squared error costterm are correctcurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod7/57train. m vl(b) Add in the weight decay term (in both the cost funcand the derivativecalculations), then re-run Gradient Checking toverify correctnessl (c) Add in the sparsity penalty term, then re-run gradiChecking toverify correctnessFeel free to change the training settings when debuggingyour%o code. (For example, reducing the training set sizecurer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod8/57train m vl/number of hidden units may make your code run fasterand setting betaand/or lambda to zero may be helpful for debuggingHowever, in yourfinal submission of the visualized weights, please useparameters web gave in Step 0 abovecoS七grad]sparseAutoencoderCost(theta, visibleSize,hiddensize, lambda,sparsityParam, beta,patches)二〓二二二二二二二〓二〓二〓二〓=二====〓=curer:YiBinYUyuyibintony@163.com,WuYiUniversityning, MATLAB Code for Sparse Autoencod9/57train.m vlll96% STeP 3: Gradient CheckingHint: If you are debugging your code, performing gradienchecking on smaller modelsand smaller training sets (e. g, using only 10 trainingexamples and 1-2 hiddenunits) may speed things upl First, lets make sure your numerical gradient computationis correct for a%o simple function. After you have implemented computeNumerun the followingcheckNumericalGradientocurer:YiBinYUyuyibintony@163.com,WuYiUniversityDeep Learning, MATLAB Code for Sparse Autoencode10/57

配电网辐射网前推回代潮流计算程序（matlab）配电网辐射网前推回代潮流计算程序（matlab）

【实例简介】很好用 经过大量的图片训练过的分类器 20*20的人脸图片训练得到