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
Online 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 MoreMATLAB Scripts for video "Linear System Identification | System Identificati...
This Github repo contains the data files and MATLAB scripts that were used in the MATLAB Tech Talk video "Linear System Identification | System Identification, Part 2"
See MoreModel Identification and Adaptive Control - From Windsurfing to Telecommunic...
This book is based on a workshop entitled: "Model Identification and Adaptive Control: From Windsurfing to Telecommunications" held in Sydney, Australia, on December 16, 2000. The workshop...
See MoreMATLAB Documentation page: idLinear mapping object
This is the Mathworks documentation page for the idLinear mapping object.
See MoreData-Driven Control: Linear System Identification
Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics.
See MoreOnline and Recursive System Identification | System Identification, Part 4
Online system identification algorithms estimate the parameters and states of a model as new data is measured and available in real-time or near real-time. Brian Douglas covers what online...
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 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 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 MoreNonlinear System Identification | System Identification, Part 3
Learn about nonlinear system identification by walking through one of the many possible model options: A nonlinear ARX model. Brian Douglas covers the importance of adding an offset term to...
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 MorePeter Ponders PID - System Identification Basics
Peter Ponders PID - System Identification Advanced
Identifying Dominant Balance Physics from Data - Jared Callaham
This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.
See MoreFrequency Response Analysis FRA and the Amplitude Ratio and Phase Angle
Process engineers model output response to inputs that oscillate via frequency response analysis (FRA). In this video, I'll go over amplitude ratios and phas...
See MoreVisually Determining Transfer Functions
Process Control classes can get pretty hard to follow when you lose sight of what transfer functions really are. How do you get them in the first place?
See MoreFinding Transfer Functions from Response Graphs
Given a system response to a unit step change, in this video I'll cover how we can derive the transfer function so we can predict how our system will respond...
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