The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction. A common approach is to start from measurements of the behavior of the system and the external influences (inputs to the system) and try to determine a mathematical relation between them without going into many details of what is actually happening inside the system; this approach is called system identification.
Topic
System Identification
This topic includes the following resources and journeys:
Type
Experience
Scope
Nonlinear Model Identification
Mathwork overview page describing nonlinear model identification. Use nonlinear model identification when a linear model does not completely capture your system dynamics. You can identify...
See MoreSystem Identification: Koopman with Control
This lecture provides an overview of the use of modern Koopman spectral theory for nonlinear control. In particular, we develop control in a coordinate system defined by eigenfunctions of...
See MoreSystem Identification: Regression Models
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...
See MoreOnline Fault Detection for a DC Motor
Program embedded processors to estimate parameters and detect changes in motor dynamics in real time using System Identification Toolbox™.
See MoreLinear Model Identification Basics
This is a curated list of Mathworks products, examples, and topics that cover identifying linear models, selecting suitable model structures, constructing and modifying model object...
See MoreWhat are Polynomial Models?
This Mathworks page provides an overview of polynomial models.
See MoreSystem Identification: Full-State Models with Control
This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...
See MoreMATLAB Documentation page: nlarx command
This is the Mathworks documentation page for the nlarx MATLAB command.
See MoreSystem Identification: Dynamic Mode Decomposition with Control
This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular...
See MoreMATLAB Documentation page: idLinear mapping object
This is the Mathworks documentation page for the idLinear mapping object.
See MoreSystem Identification: DMD Control Example
This lecture gives a Matlab example of dynamic mode decomposition with control (DMDc) for full-state system identification.
See MoreWhat are Nonlinear ARX Models?
This Mathworks page provides an overview of Nonlinear ARX Models.Nonlinear ARX models extend the linear ARX models to the nonlinear case. The structure of these models enables you to model...
See MoreIdentifying Dominant Balance Physics from Data - Jared Callaham
This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.
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