Что нового в версии 9.11 R2021b по сравнению с 9.10 R2021a
MATLAB•
Editor: Explore new capabilities for code refactoring and block editing, as well as improved code suggestions, code completion, and debugger
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Python Interface: Run Python commands and scripts from MATLAB
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BackgroundPool: Run MATLAB functions on a background thread
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Live Editor Tasks: Interactively summarize, transform, and filter groups of data (compute by group) and center and scale data (normalize data)
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HDF5: Support for HDF5 version 1.10, including Single-Writer/Multiple-Reader (SWMR), Virtual Dataset (VDS), and Metadata Cache Fine-Tuning
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Hardware: Use new apps for communicating with devices via serial (serial explorer) and TCP/IP (TCP/IP explorer)
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Projects: Organize, manage, and share your work using projects in MATLAB Online
• ode78 and ode89 Functions: Use high-order Runge-Kutta solvers for ordinary differential equations
• trenddecomp Function: Find trends in data
Simulink•
Multiple Simulations Panel: Run multiple simulations for different scenarios from Simulink Editor
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Custom Tabs: Create custom Simulink toolstrip tabs
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Mini Map: Determine context when viewing part of a block diagram
Новые компоненты•
RF PCB Toolbox - Perform electromagnetic analysis of printed circuit boards
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Signal Integrity Toolbox - Simulate and analyze high-speed serial and parallel links
Основные измененияWavelet Toolbox•
Machine Learning and Deep Learning Feature Extraction: Perform signal and image analysis, preprocessing, and feature extraction with wavelet techniques and interactive apps for AI models
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Acceleration and Deployment: Speed up wavelet algorithms using multicore processors and GPUs; generate code for desktop prototyping and embedded deployment
Symbolic Math Toolbox•
Next-Step Suggestions: Get guidance for symbolic workflows with next-step suggestions in MATLAB Live Editor
Global Optimization Toolbox•
Problem-Based Workflow: Solve nonsmooth or global optimization problems using the problem-based optimization workflow
Deep Learning Toolbox•
1D Convolutional Networks: Create and train networks for sequence and time-series data
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Deep Network Designer: Export trained networks to Simulink
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Neural ODEs: Compute deep learning solution of nonstiff ordinary differential equations using dlode45
Lidar Toolbox•
Lidar Viewer App: Visualize, analyze, and preprocess lidar point clouds interactively using the Lidar Viewer app
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Aerial Lidar Processing: Apply deep learning and SLAM algorithms on aerial lidar data
Stateflow•
Entry and Exit Junctions: Create entry and exit connections across hierarchy boundaries
System Composer•
Physical Interfaces with Simscape: Create physical interfaces, ports, and connections on components
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Software Architectures: Create software architectures from existing components
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Test Harness Support: Create test harnesses for System Composer components
Simulink Compiler•
Runtime Parameter Tuning: Tune parameters while a deployed simulation is running
Statistics and Machine Learning Toolbox•
Shallow Neural Networks: Use in Simulink as native blocks; automate hyperparameter tuning
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Machine Learning: Explore k-means clustering in a live task and use isolation forest for anomaly detection
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Deployment: Export models from the Classification and Regression Learner app to MATLAB Production Server
Simulink Control Design•
Model Reference Adaptive Control Block: Design and simulate Model Reference Adaptive Controllers
Reinforcement Learning Toolbox•
Rewards Generation: Automatically generate reward functions from controller specifications
Model Predictive Control Toolbox•
Nonlinear MPC: Implement multistage nonlinear MPC controllers with the Embotech FORCESPRO solver
System Identification Toolbox•
Nonlinear ARX Models: Create models that use regression functions based on machine learning algorithms
Predictive Maintenance Toolbox
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Diagnostic Feature Designer: Rank unlabeled features, and generate spectral features for characteristic fault frequency bands in rotating machinery
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