北航《系统辨识与自适应控制MATLAB仿真》及其源代码

matlab代码，四步相移图进行四步相移测量的程序

在win7下采用vs2015编程的modbus从机程序,c++程序，是《MODBUS软件开发实战指南》源代码2，下载后可直接运行，对使用libmodbus入门有很大帮助，在此基础上可参考libmodbus例程及其它资料实现modbus tcp编程。含libmodbus的使用方法、虚拟串口工具及《MODBUS软件开发实战指南》。

多AGV调度系统实现，代码包含5个AGV基于A*算法搜路，基于两阶段算法进行调度

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

正交频分复用(OFDM: Orthogonal Frequency Division Multiplexing)是一种多载波调制技术，早在20世纪60年代就已经提出了OFDM的概念，不过由于实现复杂度高，大家并不怎么关注，之后随着DFT(离散傅立叶变化)、FFT(快速傅立叶变换)的提出以及DSP芯片技术的发展，极大减少了OFDM实现复杂度和成本，OFDM逐步在通信领域得到了广泛的应用，并且成为了高速移动通信中的主流技术。OFDM使用相互重叠但正交的窄带传输数据，相比传统的多载波系统具有更高的频谱利用率。3gpp选择OFDM作为LTE下行数据传输制式。由于OFDM信号是多个子载波信号的叠加，所

代码实现了基于遗传算法的模糊c均值算法，用于改进FCM当中的局部收敛问题，以达到全局最优。

基于混沌映射的彩色图像加密算法分析与设计毕业论文和可执行的程序

关于无线充电方案的比较，文档中描述了几种方案的比较。

用simulink搭建整车七自由度模型，包括整车垂向，俯仰 侧倾

【实例简介】用FPGA实现红外循迹小车程序，用VHDL书写。能沿着黑色轨道前进