System Identification
System identification is a methodology for building mathematical models of dynamic systems from measured input-output data. It involves selecting a model structure, estimating parameters, and validating the model to accurately represent the system's behavior. This process is widely used in control engineering, signal processing, and data-driven modeling to understand and predict system dynamics.
Developers should learn system identification when working on projects involving control systems, predictive modeling, or data-driven analysis, such as in robotics, automotive systems, or industrial automation. It is essential for designing controllers, simulating system responses, and optimizing processes where first-principles models are unavailable or too complex.