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【实例简介】转速、电流双闭环控制直流调速系统是性能很好、应用最广的直流调速系统。根据晶闸管的特性，通过调节控制角α大小来调节电压。基于设计题目，直流电动机调速控制器选用了转速、电流双闭环调速控制电路。在设计中调速系统的主电路采用了三相全控桥整流电路来供电。本文首先确定整个设计的方案和框图。然后确定主电路的结构形式和各元部件的设计，同时对其参数的计算，包括整流变压器、晶闸管、电抗器和保护电路的参数计算。接着驱动电路的设计包括触发电路和脉冲变压器的设计。最后，即本文的重点设计直流电动机调速控制器电路，本文采用转速、电流双闭环直流调速系统为对象来设计直流电动机调速控制器。为了实现转速和电流两种负反馈分别起作用，可在系统中设置两个调节器，分别调节转速和电流，即分别引入转速负反馈和电流负反馈，二者之间实行嵌套联接。从闭环结构上看，电流环在里面，称作内环；转速环在外边，称做外环。这就形成了转速、电流双闭环调速系统。先确定其结构形式和设计各元部件，并对其参数的计算，包括给定电压、转速调节器、电流调节器、检测电路、触发电路和稳压电路的参数计算然后最后采用MATLAB/SIMULINK对整个调速系统进行了仿真分析，最后画出了调速控制电路电气原理图。

两块STM32板子之间进行串口通信，希望有帮助

FFT 基二的C语言和MATLAB实现 可以运行，但注释很好结构也很清楚，可作参考

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基于java平台，可用于WSNs网络信任机制仿真模拟，图形化界面，附带源码，只要编写节点信任值计算公式相关的类即可与经典信任机制的性能做相关比较。相关论文查阅百度文库

【实例简介】基于simulink的锁相环PLL仿真，基于simulink的锁相环PLL仿真，基于simulink的锁相环PLL仿真，基于simulink的锁相环PLL仿真

动态贝叶斯网络推理学习理论及应用(肖秦琨)，对贝叶斯网络较详细讲解

这是一个基于计算方法拉格朗日插值法实现的C语言版本的小程序，可以计算不同节点分割下的估计值及其误差值

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