MATLAB and Simulink for Artificial Intelligence

Angelika Teglasy

MATLAB and Simulink product families offer a wide range of toolboxes for signal processing, control systems, and system identification, including the Deep Learning Toolbox, which provides a framework for designing, training, and implementing neural networks, as well as the Computer Vision Toolbox, which includes algorithms, functions, and apps for image and video processing.

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Benefits of using MATLAB and Simulink to build AI-driven products

There are numerous benefits to building AI-driven products and services with MATLAB and Simulink, as outlined below.

Ease of use: MATLAB and Simulink have a user-friendly interface and a wide range of built-in functions and toolboxes that make designing, training, and deploying AI models easy.

Rapid prototyping: MATLAB and Simulink allow for fast and efficient prototyping, making it easy to test and iterate on various AI models and architectures.

Interoperability: MATLAB and Simulink integrate with other tools and programming languages, allowing seamless integration with existing systems and workflows.

Automated code generation: Simulink can automatically generate C code from models. This code can be used for deployment in embedded systems and other resource-constrained environments.

Support for a wide range of AI algorithms: Machine learning, Deep learning, Reinforcement, Regression, Unsupervised learning, Predictive maintenance, and Bayesian optimization.

Key Components of the End-to-End AI Workflow

The MATLAB end-to-end engineering AI workflow includes several steps:

Image 1. End-to-End Engineering AI Workflow
End-to-End Engineering AI Workflow

1. Data preparation:

The first and most crucial step is to collect, clean, and preprocess the data that will be used to train the AI model. This step aims to ensure that the data is accurate, efficient, and in a format that the AI model can understand.

Not all data is critical, and some data points have a more highly predictive value than others. In addition, some events are extremely rare and have to be excluded from a given dataset but still need to be modeled. Engineers are spending an enormous amount of time with the data, which they must assess, removing missing or duplicated data, and scaling and normalizing it, among other, numerous tasks. MATLAB and Simulink can make the time spent on those activities more productive and effective.

2. AI Modeling:

The next step in the workflow is to design an AI model using algorithms and pre-built models. 

Within the MATLAB environment, engineers are able to:

  • Access all the algorithms and methods used to develop models, including Machine learning, Deep learning, Reinforcement learning, Regression, Unsupervised learning, Predictive maintenance, and Bayesian optimization; 
  • Work with these algorithms and methods at the code level or through an app; 
  • Use pre-built apps to automate the training phase and add visualizations to facilitate the understanding and editing of deep networks; and
  • Accelerate training to the appropriate computing platform, and engage with the AI community.

3. Simulation and Test:

The third step in the AI workflow is to integrate the AI model which has been designed into a system-wide context, simulate it before proceeding to hardware install, and verify its effectiveness. 

Within the MATLAB environment, engineers can use Matlab and Simulink to:

  • Integrate the AI model designed into a system-wide context;  
  • Simulate the AI model to see how it performs and to make any necessary adjustments before moving on to integration with hardware; and 
  • Verify the model’s effectiveness using various metrics and compare its performance to other models or benchmarks. 

Once the AI model has been tested and evaluated thoroughly, it can be integrated into the desired application or system and deployed on the hardware.

4. Deployment:

Once the model is tested and verified, it can be integrated into the desired application or system and deployed on the hardware.  

As AI can reside in any part of systems,  engineers need an easy and fast way to deploy the AI models into any platform. Within the MATLAB environment, engineers can: 

  • Use a unique code generation framework to deploy models developed in MATLAB or Simulink anywhere without having to rewrite the original model; 
  • Use automatic code generation to eliminate coding errors; and 
  • Add MATLAB or Simulink-based projects to pre-existing databases, streaming systems, and dashboards.
Monitoring and Maintenance:

After deployment, the model must be continuously monitored and maintained to ensure it continues to work correctly.

Model DevOps (AKA “Model Ops”) is a process that organizations adopt to manage the model’s lifecycle. Essentially, it’s a blend of software development, IT, and model development practices.

Throughout this workflow, MATLAB and Simulink provide a wide range of tools and functions for data analysis, visualization, modeling, and deployment, making it an efficient and robust platform for AI development. 

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