用C语言实现简单的数据库增删改查功能

【实例简介】置信规则库的matlab代码，根据杨剑波教授构建的优化模型对参数进行优化，并和神经网络进行了对比。

Java版实验室设备管理系统 内含文档、演示文稿、源代码、数据库。开发技术Java、Sql Server。

自己写的关于循环自相关函数的快速计算方法，不同的调制方式只要更改x_t表达式即可

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

这是一个GMM的matlab集合包，包含了一共5个压缩包，每个包都是不同的GMM的matlab实现。都是在外国网站上评分很高的文档。1、EM algorithm for Gaussian mixture model。2、Gaussian Mixture Model (GMM) - Gaussian Mixture Regression (GMR)。3、Gaussian Mixture Modeling GUI (GMM DEMO)。4、gaussian_mixture_model(1D)。5、GMM

ADXL345加速度传感器SPI通信程序

simulink 永磁同步电机双闭环仿真实例，共包含四个仿真实例，均可以运行通过，还包含部分仿真资料，可供需要进行永磁同步电机仿真的工程师参考

频率估计的matlab源码，详细的程序内容和使用方法

stm32+fatfs+SDIO读写TF卡，创建文件写文件读文件，获取卡容量

用ASP.NET做的在线考试系统，用户需经过身份验证，方能进入答题库，系统随机出题，网上评卷，并可以立刻知道自己的成绩，此系统缓解教师压力。该考试系统是基于WEB技术系统开发的一种，采用B/S的三层结构模式，使用ASP.NET动态网页技术开发。不受时间和地域的限制可以随机出题。管理员创建各种试题的题库，进行试题类别和样式的定义，并对试题、成绩、用户等进行处理;教师在题库添加试题，对用户的成绩进行管理。用户则通过选择不同类别的试卷，系统随机生成试卷并开始考试，查询成绩。用户使用该系统，可以对特定学科的学习情况进行检测。