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Community and Third-Party Support
Neuroscientists using MATLAB can access a rich library of freely-shared tools built by and for researchers. Additionally, there is a vibrant market of professionally supported third-party products that complement MATLAB, including devices, instruments, application software, cloud platforms, and other research tools.
Browse the File Exchange to find community toolboxes and apps for various categories of neuroscience. Use the Add-On Explorer to easily install tools into MATLAB.
Explore third-party products for neuroscience with supported MATLAB integrations in the Connections partner directory.
MATLAB 和 Simulink 助力金属、材料和采矿
流程使用 MATLAB® 和 Simulink® 分析实时传感器数据，实施控制策略，并基于大数据和机器学习创建预测性维护系统。
MATLAB 和 Simulink 帮助采矿：
通过在高速传感器数据上应用数值技术来开发预测性维护系统
使用机器学习和历史数据来解决流程问题
使用数据建模来改进流程性能
采用数字化而非依赖于数据科学家或 IT 人员
仿真故障数据
传统上，根据传感器收集来的数据优化采矿厂和流程。
但是，传感器数据并不总适用于机器中多种可能的故障模式 。相反，您可以通过创建机器模型并仿真故障操作条件来使用仿真数据呈现故障。
Simulink 和 Simscape™ 让您构建可根据物理组件和动态描述其行为的机器模型。您可以通过修改参数值、注入故障和更改模型动态呈现机器的不同故障模式。
通过预测性维护和信号处理优化资产
MATLAB 可以帮助您根据设备的具体操作和架构特性开发自定义预测性维护算法。使用 Predictive Maintenance Toolbox™ 设计状态指示器和估算旋转钻井设备的剩余使用寿命。
您可以使用 Signal Processing Toolbox™ 自动控制回路的性能，远程确定管道中的腐蚀或点蚀程度，以及检测管道泄漏的位置和数量。
Generating Code and Targeting Hardware
Automatically generate C, HDL, or PLC code from your MATLAB and Simulink algorithms, and run them on microprocessors, FPGAs, and more.
Engineers working toward an optimized design must develop their software and physical system together. MathWorks physical modeling tools bring accuracy and efficiency to this effort by enabling you to:
Assemble system-level models that span multiple physical domains and include the control system in a single environment
Create reusable models of your physical system with physical ports, in addition to input and output signals
Model custom physical components (mechanical, electrical, and other physical domains) using a MATLAB based physical modeling language
Extend your analysis with 3D visualization and additional simulation methods
You can convert the models of your physical system to C code and deploy the code to other environments for tasks such as hardware-in-the-loop (HIL) testing. These models support all phases of Model-Based Design.
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