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Millions of engineers and scientists worldwide use MATLAB to analyze and design the systems and products transforming our world. MATLAB is in automobile active safety systems, interplanetary spacecraft, health monitoring devices, smart power grids, and LTE cellular networks. It is used for machine learning, signal processing, image processing, computer vision, communications, computational finance, control design, robotics, and much more. MATLAB 之于人工智能设计人工智能模型与人工智能驱动的系统数学·图形·编程无论是分析数据、开发算法还是创建模型，MATLAB 都是针对您的思维方式和工作内容而设计的。 MATLABMATLAB 将适合迭代分析和设计过程的桌面环境与直接表达矩阵和数组运算的编程语言相结合。专业开发MATLAB 工具箱经过专业开发、严格测试并拥有完善的帮助文档。包含交互式应用程序MATLAB 应用程序让您看到不同的算法如何处理您的数据。在您获得所需结果之前反复迭代，然后自动生成 MATLAB 程序，以便对您的工作进行重现或自动处理。以及扩展能力只需更改少量代码就能扩展您的分析在群集、GPU 和云上运行。无需重写代码或学习大数据编程和内存溢出技术。 让您的创意从研究迈向生产 部署到企业应用程序MATLAB 代码可直接用于生产，因此您可以直接部署到云和企业系统，并与数据源和业务系统集成。 在嵌入式设备上运行自动将 MATLAB 算法转换为 C/C++ 和 HDL 代码，从而在嵌入式设备上运行。 与基于模型的设计集成MATLAB 与 Simulink 配合以支持基于模型的设计，用于多域仿真、自动生成代码，以及嵌入式系统的测试和验证。 Math. Graphics. Programming.The MATLAB platform is optimized for solving engineering and scientific problems. The matrix-based MATLAB language is the world’s most natural way to express computational mathematics. Built-in graphics make it easy to visualize and gain insights from data. A vast library of prebuilt toolboxes lets you get started right away with algorithms essential to your domain. The desktop environment invites experimentation, exploration, and discovery. These MATLAB tools and capabilities are all rigorously tested and designed to work together. Scale. Integrate. Deploy.MATLAB helps you take your ideas beyond the desktop. You can run your analyses on larger data sets and scale up to clusters and clouds. MATLAB code can be integrated with other languages, enabling you to deploy algorithms and applications within web, enterprise, and production systems. Key Features- High-level language for scientific and engineering computing
- Desktop environment tuned for iterative exploration, design, and problem-solving
- Graphics for visualizing data and tools for creating custom plots
- Apps for curve fitting, data classification, signal analysis, and many other domain-specific tasks
- Add-on toolboxes for a wide range of engineering and scientific applications
- Tools for building applications with custom user interfaces
- Interfaces to C/C++, Java®, .NET, Python®, SQL, Hadoop®, and Microsoft® Excel®
- Royalty-free deployment options for sharing MATLAB programs with end users
将 MATLAB 用于： 数据分析探索如何使用 MATLAB 进行大数据、机器学习和生产分析。 无线通信探索 MATLAB 如何帮助您开发算法和执行全面的无线系统仿真。 深度学习 计算机视觉 信号处理 量化金融与风险管理 机器人 控制系统 Why MATLAB?MATLAB is the easiest and most productive software for engineers and scientists. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB provides an environment that invites exploration and discovery. It combines a high-level language with a desktop environment tuned for iterative engineering and scientific workflows. MATLAB Speaks MathThe matrix-based MATLAB language is the world’s most natural way to express computational mathematics. MATLAB supports both numeric and symbolic calculations. Linear algebra in MATLAB looks like linear algebra in a textbook; symbolic calculations look like the equations you write on paper. This makes it straightforward to capture the mathematics behind your ideas, which means your code is easier to write, easier to read and understand, and easier to maintain. You can trust the results of your computations. MATLAB, which has strong roots in the numerical analysis research community, is known for its impeccable numerics. A MathWorks team of 350 engineers continuously verifies quality by running millions of tests on the MATLAB code base every day. MATLAB does the hard work to ensure your code runs quickly. Math operations are distributed across multiple cores on your computer, library calls are heavily optimized, and all code is just-in-time compiled. You can run your algorithms in parallel by changing for-loops into parallel for-loops or by changing standard arrays into GPU or distributed arrays. Run parallel algorithms in infinitely scalable public or private clouds with no code changes. The MATLAB language also provides features of traditional programming languages, including flow control, error handling, object-oriented programming, unit testing, and source control integration. MATLAB Is Designed for Engineers and ScientistsMATLAB provides a desktop environment tuned for iterative engineering and scientific workflows. Integrated tools support simultaneous exploration of data and programs, letting you evaluate more ideas in less time. - You can interactively preview, select, and preprocess the data you want to import.
- An extensive set of built-in math functions supports your engineering and scientific analysis.
- 2D and 3D plotting functions enable you to visualize and understand your data and communicate results.
- MATLAB apps allow you to perform common engineering tasks without having to program. Visualize how different algorithms work with your data, and iterate until you’ve got the results you want.
- The integrated editing and debugging tools let you quickly explore multiple options, refine your analysis, and iterate to an optimal solution.
- You can capture your work as sharable, interactive narratives.
Comprehensive, professional documentation written by engineers and scientists is always at your fingertips to keep you productive. Reliable, real-time technical support staff answers your questions quickly. And you can tap into the knowledge and experience of over 100,000 community members and MathWorks engineers on MATLAB Central, an open exchange for MATLAB and Simulink® users. MATLAB and add-on toolboxes are integrated with each other and designed to work together. They offer professionally developed, rigorously tested, field-hardened, and fully documented functionality specifically for scientific and engineering applications MATLAB Integrates WorkflowsMajor engineering and scientific challenges require broad coordination to take ideas to implementation. Every handoff along the way adds errors and delays. MATLAB automates the entire path from research through production. You can:- Build and package custom MATLAB apps and toolboxes to share with other MATLAB users.
- Create standalone executables to share with others who do not have MATLAB.
- Integrate with C/C++, Java, .NET, and Python. Call those languages directly from MATLAB, or package MATLAB algorithms and applications for deployment within web, enterprise, and production systems.
- Convert MATLAB algorithms to C, HDL, and PLC code to run on embedded devices.
- Deploy MATLAB code to run on production Hadoop systems.
MATLAB is also a key part of Model-Based Design, which is used for multidomain simulation, physical and discrete-event simulation, and verification and code generation. 新产品和主要更新5G工具箱 模拟，分析和测试5G通信系统 深度学习工具箱 设计，训练和分析深度学习网络 金融工具箱 设计，定价和对冲复杂的金融工具 定点设计器 建模和优化定点和浮点算法 MATLAB编码器 从MATLAB®代码生成C和C ++代码 MATLAB编译器 从MATLAB程序构建独立的可执行文件和Web应用程序 MATLAB Web App服务器 将MATLAB应用程序和Simulink®仿真共享为基于浏览器的Web应用程序 R2020a的新功能 电机控制模块组 设计和实现电机控制算法 R2020a的新功能 OPC工具箱 从OPC服务器和数据历史记录读取和写入数据 Simscape流体 建模和仿真流体系统 Simulink 3D动画 可视化虚拟现实环境中的动态系统行为 Simulink编译器 作为独立的可执行文件，Web应用程序和功能样机单元（FMU）共享模拟 R2020a的新功能 Simulink覆盖范围 测量模型和生成代码中的测试覆盖率 WLAN工具箱 模拟，分析和测试WLAN通信系统 无线HDL工具箱 设计和实现用于FPGA，ASIC和SoC的5G和LTE通信子系统 What’s New in MATLAB R2020a (Version 9.8) MATLAB Web Apps MATLAB Web App Server™ lets you host MATLAB® apps and Simulink® simulations as interactive web apps. You can create apps using App Designer, package them using MATLAB Compiler™, and host them using MATLAB Web App Server. Your end-users can access and run the web apps using a browser without installing additional software. MATLAB Web App Server supports integration with authentication standards such as OpenID Connect and LDAP so that you can control access to your web apps. You can host and share multiple apps developed using different releases of MATLAB and Simulink. Simulink Compiler Simulink Compiler™ enables you to share Simulink® simulations as standalone executables. You can build the executables by packaging the compiled Simulink model and the MATLAB® code used to set up, run, and analyze a simulation. Standalone executables can be complete simulation apps that use MATLAB graphics and UIs designed with MATLAB App Designer. To co-simulate with an external simulation environment, you can generate standalone Functional Mockup Unit (FMU) binaries that adhere to the Functional Mockup Interface (FMI) standard. To provide browser-based access to your deployed simulation, you can create a web app and host it with MATLAB Web App Server™. Simulink simulations can be packaged into software components for integration with other programming languages (with MATLAB Compiler SDK™). Large-scale deployment to enterprise systems is supported through MATLAB Production Server™. To generate C and C++ source code from Simulink, use Simulink Coder™. Deep Learning Data Preparation and Labeling - Video Labeler: Label ground-truth data in a video or image sequences
- Audio Labeler: Interactively define and visualize ground-truth labels for audio datasets
- New Signal Labeler: Visualize and label signals interactively
- New Pixel label datastore: Store pixel information for 2D and 3D semantic segmentation data
- New Audio datastore: Manage large collections of audio recordings
- New Image datastore: Support for 3D data
Network Architectures - New Build advanced network architectures like GANs, Siamese networks, attention networks, and variational autoencoders
- Train a “you-only-look-once” (YOLO) v2 deep learning object detector and generate C and CUDA code
- Deep Network Designer: Graphically design and analyze deep networks and generate MATLAB code
- Custom layers support: Define new layers with multiple inputs and outputs, and specify loss functions for classification and regression
- Combine LSTM and convolutional layers for video classification and gesture recognition
Deep Learning Interoperability - Import and export models with other deep learning frameworks using the ONNX model format and generate CUDA code
- New Ability to work with MobileNet-v2, ResNet-101, Inception-v3, SqueezeNet, NASNet-Large, and Xception
- Import TensorFlow-Keras models and generate C, C++ and CUDA code
- Import DAG networks in Caffe model importer
Network Training - Automatically validate network performance, and stop training when the validation metrics stop improving
- New Train deep learning networks on 3D image data
- Perform hyperparameter tuning using Bayesian optimization
- Additional optimizers for training: Adam and RMSProp
- Train DAG networks in parallel and on multiple GPUs
- Train deep learning models on NVIDIA DGX and cloud platforms
Debugging and Visualization - DAG activations: Visualize intermediate activations for networks like ResNet-50, ResNet-101, GoogLeNet, and Inception-v3
- Monitor training progress with plots for accuracy, loss, and validation metrics
- Network Analyzer: Visualize, analyze, and find problems in network architectures before training
- New Visualize activations of LSTM networks and use Grad-CAM to understand classification decisions
Deployment - New Generate code for networks such as YOLO V2 object detector, DeepLab-v3+, MobileNet-v2, Xception, DenseNet-201, and recurrent networks
- New Deploy deep learning networks to ARM Mali GPUs
- New Automated deployment to Jetson AGX Xavier and Jetson Nano platforms
- Apply CUDA optimized transposes using shared memory for improved performance
Reinforcement Learning - New Reinforcement Learning Algorithms: Train deep neural network policies using DQN, DDPG, A2C, PPO, and other algorithms
- Environment Modeling: Create MATLAB and Simulink models to represent environments and provide observation and reward signals for training policies
- Training Acceleration: Parallelize policy training on GPUs and multicore CPUs
- New Reference Examples: Implement policies for automated driving, robotics, and control design applications
Wireless Communications 5G and LTE Mobile Communications Standards - New 5G support in Wireless Waveform Generator App: Generate NR-TM, and uplink and downlink FRC waveforms using the Wireless Waveform Generator app
- New Support for PRACH physical channels: Model physical random access channel (PRACH) used in initial system access
- Support for SRS, DM-RS and PT-RS signals: Model 5G signals used for uplink channel sounding, channel estimation and phase tracking
- New Deep learning data synthesis for 5G channel estimation: Generate deep learning training data for convolutional neural networks (CNN) used in 5G channel estimation
- NB-IoT Uplink Shared Channel Modeling: Generate and decode the narrowband Internet of Things (NB-IoT) uplink shared channel
WLAN and Connectivity Standards - New System-level simulation Examples: Model an 802.11ax downlink orthogonal frequency-division multiple access (OFDMA) scenario, multiple space-time streams, and 802.11a Minstrel rate adaptation
- New Support for IEEE 802.11ax Draft 4.1 (Wi-Fi6): Generate high-efficiency single-user null data packets (NDPs) with preamble puncturing, as defined in IEEE® P802.11ax™ Draft 4.1
- New Link-level simulation of IEEE 802.11ax Trigger-Based Format: Configure, generate, demodulate and decode high-efficiency trigger-based (HE TB) waveforms
- New Blind Signal Recovery and Analysis Example: blindly detect, decode and analyze multiple IEEE 802.11a and IEEE 802.11ax packets in a waveform
- New Bluetooth Low Energy BR/EDR waveform generation and link-level simulation: Generate, demodulate, and decode Bluetooth® basic rate (BR)and extended data rate (EDR) PHY waveforms
- Bluetooth Support in Wireless Waveform Generator App: Generate and export Bluetooth Low Energy waveforms from the Wireless Waveform Generator app
- Bluetooth Low Energy (BLE) Examples: Simulate BLE coexistence with WLAN, and perform BLE RF-PHY blocking, intermodulation, and carrier to interference (C/I) performance receiver tests
Massive MIMO, Multi-User MIMO, and Beamforming - New Multiuser Block Diagonalization Beamforming: Compute precoding and combining weights for multiuser MIMO systems
- Massive MIMO: Simulate an end-to-end MIMO link using hybrid beamforming
- New Transmit and Receive Signals with Unlimited Antennas: Apply WLAN transmission, multipath channel modeling and receiver operations with arbitrary number of antennas and links
- Wireless LAN: 802.11ad waveform generation with beamforming
Channel and Propagation Modeling - New Ray Tracing Propagation Model: New propagation model using ray tracing method of images with material reflection loss
- New RF propagation using ray tracing: Predict the total received power and generate coverage maps with ray tracing
- New Rain Attenuation Models: Predict signal attenuation with Global Crane Rain Attenuation and ITU models
- RF Propagation Visualization with Ray Tracing: Configure and visualize transmitter and receiver sites, buildings, links, ray tracing results, and coverage maps using free-space, terrain, and weather-effects propagation models
- SINR Visualization: Visualize transmitter site signal-to-interference-plus-noise ratio (SINR) on a map
RF and Digital Front End - Power amplifier (PA) modeling: Model wideband and narrowband power amplifiers, capturing non-linearity and memory effects based on input/output device characteristics
- S-parameter block: Model frequency response of RF devices using S-parameter data
- Linearize power amplifiers with DPD: Simulate linearization of RF power amplifiers with memory using digital predistortion (DPD)
- RF budget analyzer: Analytically compute gain, noise figure, and IP3 for cascaded RF components, and visualize using Smith and polar plots
Antenna Modeling - New Custom Antenna Patterns: Import custom patterns expressed in phi-theta coordinates using Sensor Array Analyzer app
- Antenna designer: Interactively select and analyze antennas with desired characteristics
- PCB stack antenna: Design custom PCB antennas with arbitrary metal-dielectric layers and advanced meshing control
- Gerber file generation: Prototype and implement antennas using a customizable library of RF connectors and PCB manufacturing services
Software-Defined Radio - Standard-compliant LTE and WLAN: Over-the-air waveform generation and capture
- USRP E300 Series software-defined radio: Prototype and test wireless system designs on Ettus Research USRP E-300 SDRs
- ADALM-PLUTO software-defined radio: Prototype and test wireless system designs on Analog Devices PlutoSDR
C/C++ Code Generation - MATLAB Coder: Generate C++ classes from MATLAB classes
- Embedded Coder: Generate C/C++ Code for Software Compositions with Message-Based Communication
- Fixed-Point Designer: Explore signal ranges and convert Simulink models using data type optimization
Automotive Perception System Design - New Lidar Sensor Model: Generate synthetic point clouds from programmatic driving scenarios
- New Tracking Examples: Fuse radar and lidar tracks, perform track-to-track fusion in Simulink
- Unreal Engine® Compatible Sensor Models: Integrate your Simulink model with a camera, lidar, or radar sensor model simulating in an Unreal Engine scene
- Monocular Camera Parameter Estimation: Configure a monocular camera by estimating its extrinsic parameters
- Radar Sensor Model Enhancements: Model occlusions in radar sensors
- Sensor fusion and tracking examples
- Path Planning: Plan driving paths using an RRT* path planner and costmap
- Lidar Segmentation: Quickly segment 3D point clouds from lidar
Test and Verification - New MDF Read on Linux: Open and read MDF files on Linux platform
- MDF File Information and Sorting Functions: Quickly access MDF file metadata and sort the contents of an MDF file
- MDF File Import Performance: Open and read MDF files significantly faster than in previous releases
- Kinematics and Compliance Virtual Test Laboratory: Generate mapped suspension calibration parameters from spreadsheet data
- Vector BLF File Format Support: Read and write binary BLF logging files from MATLAB
- Prebuilt Driving Scenarios: Test driving algorithms using Euro NCAP® and other prebuilt scenarios
- OpenDRIVE® File Import Support: Load OpenDRIVE roads into a driving scenario
- Driving Scenario Designer: Interactively define actors and driving scenarios to test controllers and sensor fusion algorithms
- Preassembled maneuvers for common ride and handling tests, including a double-lane change and constant radius test
Ground Truth Labeling - New Lidar labeling: Label lidar point clouds to train deep learning models
- New Multisignal Ground Truth Labeling: Label multiple lidar and video signals simultaneously
- Ground Truth Labeling: Organize labels by logical groups
- Define multiple custom labels in Ground Truth Labeler connector
- Ground Truth Pixel Labeling: Interactively label individual pixels in video data
- Ground Truth Label Attributes: Organize and classify ground truth labels using attributes and sublabels
Visualization - New 3D Simulation Version Upgrade: Run 3D simulations using Unreal Engine, Version 4.23
- New Headless Mode: Run 3D simulations without opening the Unreal Engine 3D visualization display
- 3D Simulation: Develop, test, and verify driving algorithms in a 3D simulation environment rendered using the Unreal Engine by Epic Games®
- Unreal Engine Scenes: Use prebuilt 3D scenes, including a parking lot, highway segment, and Mcity, or create your own custom scene with the Unreal Editor
- HERE HD Live Map Reader: Read and visualize data from high-definition maps designed for automated driving applications
- Unreal Engine 4 Interface: Use support package to customize and install additional 3D scenes
- Maneuver Reference Applications: Use 3D environment ray tracing to determine ground location under tires during vehicle maneuver
- Bird's-Eye Scope for Simulink: Analyze sensor coverages, detections, and tracks in your model
Electrification - Virtual Calibration: Use Model-Based Calibration Toolbox™ to calibrate Mapped Motor and Three-Phase Voltage Source Inverter block efficiency maps with measured data
- Getting Started Example: Generate current controller calibration tables for flux-based motor controllers
- Libraries of propulsion, steering, suspension, vehicle body, brake, and tire components
- Flux-Based Motor Parameterization: Generate parameters for Flux-Based PMSM and Flux-Based PM Controller blocks
- Battery Parameterization: Generate parameters for Datasheet Battery and Equivalent Circuit Battery blocks
Engine Calibration - Deep Learning Engine Model: Generate a deep learning engine model for algorithm design and performance, fuel economy, and emissions analysis
- ASAM CDFX File Format Support: Import, export, and modify files in calibration data file format (CDFX)
- Timestamp Support for XCP Blocks: Communicate timestamped data between Simulink models and XCP slaves
- Virtual Calibration: Use Model-Based Calibration Toolbox to calibrate SI and CI mapped engine blocks
- CI and SI Engine Dynamometer Reference Applications: Resize engines and recalibrate controllers based on desired engine displacement and number of cylinders
Fuel Economy and Performance Analysis - New Vehicle and Trailer Blocks: Implement 3DOF trailers and vehicles with three axles
- New Transmission Control Module: Optimize shift schedules for algorithm design and performance, fuel economy, and emissions analysis
- HEV Reference Applications: New or updated reference applications for single-motor HEV architectures P0, P1, P2, P3, and P4. Fully assembled models use a new equivalent consumption minimization strategy (ECMS) for the supervisory hybrid control
- Powertrain Efficiency: Evaluate and report energy and power losses at the component and system level
- HEV Input Power-Split Reference Application: Use fully assembled model for HIL testing, tradeoff analysis, and control parameter optimization of a power-split hybrid like the Toyota Prius
AUTOSAR - New AUTOSAR Adaptive Platform Release 19-03: Use the 000047 (R19-03) schema to import and export ARXML files and generate AUTOSAR-compatible C ++ code
- Blocks for Basic Software Services: Use blocks for modeling and simulating Basic Software services, including Diagnostic Event , Function Inhibition and NVRAM Manager blocks
- Create AUTOSAR Architecture Models: Author AUTOSAR compositions, view component/composition dependencies via spotlights and simulate functional behavior with basic software services using Composition Editor (requires System Composer™). Then you can generate and package composition arxml descriptions and component code (requires Embedded Coder®).
- AUTOSAR SW-C App: Easily configure a model as AUTOSAR classic component or adaptive application
Motor Control Blockset Motor Control Blockset™ provides Simulink® blocks that let you create an accurate motor model. The blockset also provides tools for collecting data directly from hardware and calculating motor parameters. MATLAB R2020a的系统要求 操作系统 Windows 10（1709版或更高版本） Windows 7 Service Pack 1 Windows Server 2019 Windows Server 2016 处理器 最低要求：任何Intel或AMD x86-64处理器 推荐：具有四个逻辑核心和AVX2指令集支持的任何Intel或AMD x86-64处理器 磁碟 最小：3 GB的HDD空间仅用于MATLAB，典型安装为5-8 GB 推荐：推荐使用SSD 完整安装所有MathWorks产品可能会占用多达31 GB的磁盘空间 内存 最小：4 GB 推荐：8 GB 对于Polyspace，建议每个内核4 GB 图形 不需要特定的图形卡。 建议使用硬件加速的图形卡，该图形卡支持带有1GB GPU内存的OpenGL 3.3。 使用并行计算工具箱的GPU加速需要支持CUDA 3或更高版本的GPU。 请参阅维基百科，以确定您的GPU支持的CUDA版本。 R2020a R2020a新增功能，错误修复，兼容性注意事项展开全部R2020a：错误修复环境Profiler Flame Graphs：直观地调查和提高代码的性能实时编辑器循环执行：在实时脚本中运行循环时，性能得到改善实时编辑器动画输出：改进实时脚本中的动画时的性能实时编辑器的响应能力：通过长期使用来提高性能Live Editor控件值更改：对值更改运行所有必要的代码文件编码：默认情况下，将MATLAB代码文件（.m）和其他纯文本文件另存为UTF-8编码的文件帮助浏览器中的多个源：在单个浏览器中一起搜索MathWorks文档和自定义文档Web文档：无需登录即可在Web上查看MathWorks文档。国际化：UTF-8作为Mac和Windows平台上的系统编码语言与程序设计开关功能：更灵活地比较对象copyfile和movefile功能：访问基于Web的存储服务，例如Amazon Web Services和Azure Blob存储dbup和dbdown命令：一步切换工作空间bin2dec和hex2dec函数：转换包含二进制或十六进制前缀和后缀的文本dec2bin和dec2hex函数：转换负数复杂函数：创建稀疏复杂数组枚举类：隐藏成员名称以进行兼容的名称更改matlab.mixin.SetGet：设置部分属性名称匹配的优先级类逻辑转换：编写类时更灵活地支持逻辑转换功能被删除或更改数据分析实时编辑器任务：交互式地处理表格和时间表，并生成代码基本拟合工具：使用现代化的界面将线拟合到绘制的数据detrend功能：忽略NaN值accumarray功能：在所有平台上保持一致的输出顺序second秒功能：列出日期时间数据类型使用的所有leap秒时区功能：确定IANA时区数据库版本namedvars功能：重命名表或时间表中的变量rows2vars和unstack功能：使用命名规则允许表和时间表变量名称包含任何字符包含范围，重叠范围和范围内函数：确定时间表行时间是否与指定时间范围相交高大数组：在高大数组上操作，具有更多功能，包括groupfilter和match功能被删除或更改数据导入导出数据存储：使用writeall将数据从数据存储写入文件数据存储：从tabularTextDatastore和电子表格数据存储对象返回时间表数据存储：对TransformedDatastore和CombinedDatastore对象进行分区和混洗数据存储：使用FileSet和BlockedFileSet对象迭代处理文件和文件中的块Parquet文件：写入文件时控制编码方案和Parquet版本文本和电子表格文件：使用“ WriteMode”参数附加，覆盖或替换数据readtable函数：默认情况下使用detectImportOptions函数的结果textscan，readtable，detectImportOptions和setvaropts函数：读取和导入十六进制和二进制文字h5read和h5readatt：将非标量字符串数据读取为MATLAB字符串数组h5create和h5write：将字符串数据写入HDF5文件CDF库：升级到v3.7.0Tiff对象：读写有理多项式标记的值jsonencode：自定义MATLAB类中的编码jsonencode：编码枚举功能被删除或更改数学nufft和nufftn函数：计算非均匀快速傅立叶变换稀疏函数：支持整数下标和逻辑聚合图形boxchart功能：通过使用箱形图可视化分组的数值数据exportgraphics和copygraphics功能：保存和复制图形，改进了对发布工作流的支持ChartContainer类：开发显示直角坐标图，极坐标图或地理图的图平铺图表布局：定位，嵌套和更改布局的网格大小pie功能：为百分比标签指定数字格式轴便利功能：将轴或图表对象的数组传递给便利功能，例如网格，保持和框SeriesIndex和NextSeriesIndex属性：控制绘图如何在颜色和线条样式之间循环colororder功能：控制散布直方图和平行图中的颜色pareto功能：指定要包括的累积直方图的分数轴：通过设置InnerPosition和PositionContraint属性来控制标题和标签的边距内置轴交互：使用显示可用交互的光标浏览数据内置轴互动：自定义地理轴上的内置互动linkdata功能：打开对话框以使用新语法指定数据源功能被删除或更改应用程式建立uicontextmenu功能：在应用程序和App Designer画布上添加和配置上下文菜单组件uitoolbar功能：将自定义工具栏添加到应用程序关于激活安装MathWorks MATLAB R2020a 之前请先卸载之前安装的旧版版本 1、win7用户加载R20120a_Windows.iso镜像文件，win10直接打开镜像，双击setup.exe安装软件 2、安装过程选择“使用文件密钥安装”，输入序列号(压缩包内有) 3、自定义软件安装目录以及要安装的产品组件，完成软件的安装。 4、软件安装完成后，先不要打开软件，将R2020a文件夹复制替换到安装目录内 默认：C:\Program Files\Polyspace 5、复制license_standalone.lic到软件安装目录下的licenses文件夹内（如果没有licenses文件夹，就自己创建一个） 6、完成激活，运行就是破解版了   本部分内容设定了隐藏，需要回复后才能看到 20G。已经上传百度网盘，没有网盘会员的，这里推荐一款网盘不限速工具： http://bbs.sdbeta.com/read-htm-tid-574140.html或者更多工具 百度网盘满速下载(5款百度网盘破解不限速工具) ： https://m.sdbeta.com/article/2020/0305/234004.html
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