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订阅专栏后，可直接查看源代码，粘贴到MATLAB空脚本中即可直接运行、得到结果
本例程面向城市干线多交叉口绿波速度引导问题，利用多个交叉口的信号相位与相位差信息，为车辆生成速度建议，对比无引导和V2X绿波引导下的停车次数、总延误和通过时间。程序适合用于车路协同绿波通行、干线速度引导和信号协同控制研究。
绿波速度引导图：对比无引导和V2X绿波引导下的速度变化。
时距轨迹图：展示车辆沿干线通过多个交叉口的时间-距离轨迹。
停车次数对比图：比较两种策略下的停车次数。
总延误对比图：比较无引导和V2X绿波引导的总延误。
交叉口通
【MATLAB代码，无人机避障7】基于RRT*与人工势场法（RRT*+APF）的多无人机二维动态路径规划与避障。订阅专栏后可查看完整代码
本文基于RRT与人工势场法（APF）实现四架无人机在二维动态障碍环境中的协同路径规划与避障。通过RRT优化全局路径，结合APF进行局部避碰，支持参数自定义，提供完整MATLAB代码，可直接运行并生成轨迹、安全净间隙、速度加速度及航程等可视化结果，对比RRT+APF与RRT*+APF性能，适用于科研与教学。
【MATLAB代码】基于改进人工势场法（Improved APF）的多无人机动态路径规划与避障｜切向绕障、局部极小值改善及协同避碰仿真（含公式）
本文提出一种改进型人工势场法（APF），通过引入切向引导与目标距离权重，提升4架无人机在动态障碍环境中的路径规划效率与安全性。相比基础APF，改进方法在相同约束下实现更快收敛（27.6s vs 36.6s）、更优航程（364.26m vs 366.56m），且保持0碰撞、0违规。所有结果可复现，代码已封装为独立MATLAB脚本，订阅专栏后即可直接运行。如需定制或深入讲解，欢迎联系作者。
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本程序基于MATLAB构建永磁同步电机（PMSM）仿真模型，采用无迹卡尔曼滤波（UKF）实现电流、转速与电角度的联合估计。通过缩放无迹变换生成Sigma点，避免非线性线性化误差，并针对电角度周期性边界进行特殊处理。仿真验证了在转速变化工况下，UKF对状态变量的高精度估计能力，结果表明其在转速与角度跟踪、电流滤波及新息诊断方面表现优异，具备良好的鲁棒性与实用性，适用于非线性状态估计与电机控制研究。
linux操作系统使用以太网连接ubuntu，设置固定ipv4
首先，您需要确定要配置的网络接口。可以使用ifconfig -a或ip addr命令来列出当前系统上的所有网络接口及其相关信息。找到您要配置的网络接口名称，例如eth0、ens33或enp0s3；保存并关闭文件后，您需要重启网络服务以使更改生效。在linux操作系统中，要使用交换机连接以太网可以直接使用交互界面；如果你的操作系统没有交互界面，你可以使用命令方式配置；点击ipv4，方式为手动，可以设置ip和子网了；开头，后接网络接口名称。第三步：点击有线最右边圆形控件；第二步：点击有线设置；
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本文详细介绍了robotics-toolbox-python工具箱，从基础安装、环境配置到机器人运动学与动力学的核心应用。通过D-H参数、URDF导入和内置模型三种方式快速构建机器人模型，并演示了正逆运动学计算、关节空间与任务空间轨迹规划以及三维可视化仿真。该工具箱极大地简化了机器人算法开发流程，是进行机器人学研究和应用实践的强大工具。
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EPLAN中2D安装板的绘制方法示例
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Unity如何实现3D物体拆解组装
最近有一个需求，是做一个发动机的拆卸和安装功能，其实是一个很简单的功能，但是其中有一个点我觉的非常有意思，就是拖拽组装时，物体如何精准拖到目标位置，思路有了，但是我一直找不到实现方式，早晨刷牙时无意间想到了叉乘，我才有了解决方案。就凭这一次的灵光乍现，也很有必要写篇博客来记录一下。首先，我们先来看下我们要实现的效果，就是对一个发动机（任意多组件物体，如：车）的360°预览查看，手动拖拽拆解，手动拖拽组装。360°旋转视角远近拉伸视角点击拖拽拆卸拖拽组装。
TMS_WebGMaps_1.9.8.0_Full_Source
TMS_WebGMaps_1.9.8.0_Full_Source 全源码 delphi cbuilder
深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
ET2012新版免狗免安装绿色软件
ET2012新版免狗免安祖昂绿色软件。，新界面，新视觉，
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