Artificial Intelligence

Virtual Sensor Modeling

Estimate signals of interest that a physical sensor cannot directly measure, or when a physical sensor adds too much cost and complexity to the design.

System Identification and ROM

Create AI-based models of nonlinear dynamic systems by using measured or generated data.

Reinforcement Learning

Train intelligent agents through repeated trial-and-error interactions with dynamic environments modeled in Simulink.

Why MATLAB and Simulink for Designing AI into Engineered Systems?

Integrate and simulate AI models with the rest of the system

Achieve safety and reliability of AI-enabled systems in operation

Generate code from AI models to target different hardware

Generate and deploy C/C++, CUDA®, and HDL code from deep learning or machine learning models that runs on supported target hardware.

Manage deployment trade-offs of embedded AI

  • Profile model size, speed, and accuracy in simulation and code
  • Compare differences in performance of different AI models and AI versus non-AI models.
  • Assess impact of model compression
  • Leverage results of analysis to inform model selection, make design decisions, and fine-tune model behavior.