墨衍会员
·
AI 创作全网分发
墨衍智能分发
本文由作者通过墨衍一键同步至各平台
1分发平台
100+累计阅读
50平均单平台
已同步平台
优快云
微信公众号
微博
知乎
掘金
百家号
博客园
抖音
小红书
你的文章也可以这样分发
墨衍会员 ¥399起/年，写一次发全网
我也要
💥💥💥💥💥💥💥💥💞💞💞💞💞💞💞💞💞Matlab武动乾坤博客之家💞💞💞💞💞💞💞💞💞💥💥💥💥💥💥💥💥
🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚤🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀
🔊博主简介：985研究生，Matlab领域科研开发者；
🚅座右铭：行百里者，半于九十。
🏆代码获取方式：
优快云 Matlab武动乾坤—代码获取方式
更多Matlab智能算法优化神经网络分类预测仿真内容点击👇
①付费专栏智能算法优化神经网络分类预测
⛳️关注优快云 Matlab武动乾坤，更多资源等你来！！
1 CNN-BiGRU-SAM-
【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。
【PMSM例程11】永磁同步电机（PMSM）基于UKF的转速与转子位置估计。附下载链接
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。
泰克示波器TDS210改装便携示波器
泰克示波器TDS210改装编写示波器
win11系统ping另一台电脑上VMware虚拟机的IP
我是用了两台电脑，所以刚才遇到一个问题就是两台电脑的IP 在同一局域网内ping不通。于是就找了很多资料，经过坚强的绝望后终于解决了。 下开始解决问题： VMWare提供了三种工作模式：bridged(桥接模式)、NAT(网络地址转换模式)和host-only(主机模式)。 1. bridged(桥接模式) 在这种模式下，VMWare虚拟出来的操作系统就像是局域网中的一台独立主机，它可以访问网内任何一台机器。在桥接模式下，你需要手工为虚拟系统配置IP地址、子网掩码，而且还要和宿主机器处于同一网段，这样虚
ARM和FPGA之间的通信（硬件原理图篇）
设计该开发板的初衷是为了学习，但也希望今后能扩展其功能，看到大家都在diy示波器，自己也非常冲动的想DIY一把，于是呼选了arm+fpga的方案，fpga主要用来实现数据的高速采样，而ARM则提供数据的显示，以及一些人机交互的功能。那么该方案中最重要的地方就是ARM和FPGA之间的通信如何实现了。在网上查了很多的资料，有说将FPGA作为ARM的一个外设，可以通过DMA的方式来直接访问；也有说把FP
SoC 验证老兵手册
《SoC验证老兵手册》摘要： 针对大型SoC系统级验证，手册提出Cache+DMA场景的高效验证方案。核心方法采用C测试用例（模拟Linux/RTOS真实场景）触发隐蔽bug，配合SV Checker精准捕捉硬件问题（如DMA读取脏缓存行）。特别强调验证方法论：C用例负责复杂行为模拟，SV专注RTL正确性检查；利用Linux天然并发性暴露硬件缺陷。手册还包含模拟面试攻防技巧，指出验证架构师需具备定义Stimulus/Checker边界的能力。终极判断标准：未通过Linux下Cache+DMA+SMP+中断测
Crush 终端 LLM 工具安装与使用指南：把 AI 编程助手装进你的命令行
喜欢命令行的开发者需要离线/轻量级 AI 助手的场景希望自定义模型与工具链的高级用户通过LSP + MCP + 多模型支持，Crush 不仅能写代码，还能理解项目、调用工具、连接服务，真正成为你的“终端编程搭档”。
CloudCompare 二次开发(6)——插件中拖拽添加Qt窗口（区域生长算法为例）
手动拖拽的方式搭建Qt对话框界面的制作流程，以PCL中的点云区域生长算法为例进行制作。
适航规章、标准，书籍资料下载汇总（增加DO-297 IMA）
从事IMA平台适航工作的朋友看过来，DO-297英文版PDF可以下载了！更新日志：2024-9-28：初版。2024-10-21：增加DO-330英文版PDF下载，后台回复关键词”330“获取下载链接。2024-10-30：增加DO-160G中文版（非机翻）和英文版PDF下载，后台回复关键词”160“获取下载链接。2024-11-10：增加DO-297英文版PDF下载，后台回复关键词”297“获取下载链接。以下是一些收集到的适航相关的资料，分享给大家。
2.0 电子凸轮 西门子1500电子凸轮使用研究1
本文研究了S7-1500T电子凸轮同步控制技术，通过仿真实验得出以下结论：1）通过比例系数可调整凸轮曲线形状；2）解除同步需调用从轴其他运动指令；3）详细阐述了凸轮同步的5个实施步骤。重点分析了三种同步类型（主值距离同步、动态参数同步和直接同步）的特点及应用场景，并探讨了缩放系数、偏移量等参数对凸轮曲线的影响。同时介绍了凸轮应用模式及常见异常处理方法，为工程应用提供了重要参考。
LinePhone3.2.7(android端最新版本) sip客户端
SIP客户端For android：LinePhone3.2.7(最新版本)，官网地址：http://www.linphone.org/
VB摄像头监控
该VB模块可对摄像头捕获的画面进行录像等，可在创建的窗口进行控制
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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")
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
订阅专栏 解锁全文
180
扫一扫
