labview生成spwm波

【实例简介】利用python载入邻接矩阵绘制网络图，基于python语言的特点，对邻接矩阵加以处理后再进行应用，即将邻接矩阵去除第一列（节点序号列），复杂网络的基本拓扑结构可以用图论的方法表示成G =(V，E)，V中元素称为节点或顶点，E中元素称为边。在图论框架下, 可用不同的全局参量来表示复杂网络的特征, 最基本特征包括度分布 P(k)和聚类系数C。

制冷剂物性计算软件，很多制冷剂的状态轻松计算，制冷行业的用得上的哦

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

基于LabVIEW的五子棋游戏 目录摘要Abstract第1章  前言  1第2章  LabVIEW简介  22.1 LabVIEW的概念. 22.2 LabVIEW的特点. 2第3章 总体设计方案  33.1五子棋游戏规则. 33.2游戏设计框图. 33.3游戏设计流程图. 43.3.1总体设计流程图. 43.3.2人机对弈模式下的游戏流程图. 43.3.3双人对弈模式下的游戏流程图. 5第4章 各模块程序设计  74.1初始棋盘模块. 74.2多步计算模块. 74.2.1多步计算流程图. 74.2.2多步计算前面板及程序框图. 84.3决定下子方模块. 104.4判定胜负模块. 104.4.1判定胜负模块设计前面板及程序框图. 104.5胜负对话模块. 11第5章 主程序设计  145.1游戏主程序设计. 145.1.1五子棋主程序的程序框图. 145.1.2五子棋的游戏界面. 155.2结果演示. 16第6章  结束语  17参考文献  18答谢辞  19

基于camshift均值漂移算法， kalman卡尔曼滤波算法及LBP特征的目标跟随算法，配置完成 opencv路径即可运行。LBP特征跟踪非常的稳定，对于颜色差别不大的区域也能流程跟随，比如跟踪手能够平滑的经过脸部区域而不漂移。工程为集中集中算法融合优化，非常适合学习及工程实践。

PyQt5系列教程(二)利用QtDesigner设计UI界面源码包，博文地址http://blog.csdn.net/djstavaV/article/details/50282527

一个用WPF编写的串口助手小程序，可实现串口的打开、写入、读取和关闭等功能，具有响应和应答两种模式，有详细的代码注释哦

天气预报的PNG图片包，本图片包包含了常见的30种天气类型。

MATLAB代码，图像质量评价标准，FSIM

lqr轨迹跟踪的matlab编程，是通过lqr控制器控制，走出一条直线或者圆轨迹