Linear System Identification | System Identification, Part 2 скачать в хорошем качестве

Linear System Identification | System Identification, Part 2

Learn how to use system identification to fit and validate a linear model to data that has been corrupted by noise and external disturbances. Watch the full series on System Identification:    • System Identification   Noise and disturbances can make it difficult to determine if the error between an identified model and the real data comes from incorrectly modeled essential dynamics, data influenced by a random disturbance process, or some combination of the two. Discover how to account for the random disturbances by fitting a first-order autoregressive moving average (ARMA1) model to the disturbance path. This can give you a better overall system model fit and confidence that the essential dynamics were captured correctly. Check out these other links: All of the references below are displayed in a journey on Resourcium: https://bit.ly/382c5BQ Introduction to System Identification:    • Introduction to System Identification   System Identification Overview: https://bit.ly/3LiDlKE Linear Model Identification Basics: https://bit.ly/3OA7WFL What is Residual Analysis? https://bit.ly/3xR3dJH resid command in MATLAB: https://bit.ly/3Keh0fW Goodness of fit: https://bit.ly/3vfrF65 -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.