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

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/3c7WL5e - Introduction to System Identification: https://youtu.be/u7hJ1aF-JrU - System Identification Overview: https://bit.ly/3ky2ANJ - Linear Model Identification Basics: https://bit.ly/3Hdyby2 - What is Residual Analysis? https://bit.ly/3qu6MSA - resid command in MATLAB: https://bit.ly/3HfGp8Q - Goodness of fit: https://bit.ly/3nbDPJc