Virtual sensor modeling is a great technique that is often used to simulate physical sensor behavior. Virtual sensors (or soft sensors) can be helpful when engineers cannot measure the signal of interest or when adding a physical sensor would make the design too expensive or complicated.
AI techniques can be used as an alternative to Kalman filters or many other virtual sensing techniques. However, an AI-based virtual sensor model represents only a small piece of a larger system and must be integrated, implemented, and tested with all other components together while minimizing expensive and time-consuming prototyping with actual hardware. Model-Based Design is a proven approach to accomplish this.
During this webinar, you will learn:
- How to use AI with Model-Based Design to make the complexity of various systems more manageable
- How to integrate AI models into Simulink for system-level simulation and code generation in the context of modeling a virtual sensor
- How to speed up the simulation of complex Simulink Models by leveraging AI-based Reduced Order Models.
- What is AI Workflow?
- Machine Learning and Deep Learning Usecases
- AutoML Solutions
- Virtual Sensors
- Reduced Order Modeling
Join this webinar to:
- Learn how MATLAB, Simulink, and Model-Based Design enable you to take advantage of disruptive technologies like AI
- Gain a clear overview of different methods and solutions that MATLAB/Simulink has to offer in creating Virtual Sensors and Reduced Order Modeling.
Who should attend:
- Simulink users who want to learn how to use AI to streamline their system design.
- Systems / MBD engineers who want to develop embedded algorithms
- Test engineers / HIL Engineers who want to develop MBD models and test in a real-time environment using simulation (Simulink real-time / Speedgoat)
Please allow approximately 50 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can’t make it for the live broadcast, register and we will send you a link to watch it on-demand.