System identification is the process of using data rather than physics to develop a model of a dynamic system. In this series, discover different ways to approach system identification for linear systems, nonlinear systems, and for online and recursive system identification. Throughout the series, see the system identification workflow through several different examples that highlight the importance of data collection, model selection, model fitting, and model validation.
Part 1: What Is System Identification? System identification is the process of using data rather than physics to develop a model of a dynamic system. Explore what system identification is and where it fits in the bigger picture.
Part 2: Linear System Identification Learn how to use system identification to fit and validate a linear model to data that has been corrupted by noise and external disturbances.
Part 3: Nonlinear System Identification Learn about nonlinear system identification by walking through one of the many possible model options: A nonlinear ARX model.
Part 4: Online and Recursive System Identification Learn about online system identification. These algorithms estimate the parameters and states of a model as new data is measured and available in real-time or near real-time.