Data-Driven Modeling: Nonlinear System Identification using AI Techniques
In this webinar we will provide a brief overview of System Identification toolbox and focus on identifying nonlinear models to capture nonlinear system dynamics.
You will learn how to create models of nonlinear system dynamics from data for system simulation, reduced order modeling, and control design workflows. You will learn how to combine your knowledge and intuition about system dynamics with AI techniques.
The webinar will cover identification of nonlinear ARX and Hammerstein-Wiener models that leverage machine learning methods such as Support Vector Machines (SVM), Gaussian Processes (GP), and other representations. You will see how you can choose regressors that best capture your system dynamics, and how you can develop nonlinear dynamics models by first identifying linear models and then adding nonlinear effects.
You will also learn how to identify neural state space models that describe system dynamics in the familiar state-space form, but with nonlinear state and output update functions that are represented by neural networks learned from data.
In this webinar you will learn about:
- Overview of system Identification
- Nonlinear system identification using Nonlinear ARX, Hammerstein-Wiener, and Neural ODE models
- Available AI techniques to describe nonlinearities in your model
- Use of nonlinear models for system simulation, control design, and reduced order modeling
About the Presenter
Kishen Mahadevan is a Product Manager for Fuzzy Logic Toolbox, System Identification Toolbox, and Simulink Design Optimization at MathWorks. Kishen has an M.S. in Electrical Engineering with specialization in Control Systems from Arizona State University, and a B.E. in Electrical and Electronics Engineering from Visvesvaraya Technological University in India. He joined MathWorks in 2018 as an Application Support Engineer helping customers resolve issues related to Simulink products before moving into product marketing.
Recorded: 14 Nov 2022
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