Modeling Dynamic Systems from Measured Data

Understanding how physical, engineering, or economic systems behave requires accurate mathematical models. System identification provides the essential tools to derive these models directly from observed input-output data. This course teaches you the core methodologies for identifying dynamic systems, enabling you to construct reliable predictive models for control, simulation, and analysis without relying solely on complex theoretical derivations. What you'll learn: * Understand the core concepts of dynamic systems, linear time-invariant (LTI) models, and the complete system identification workflow. * Apply essential methods for data preparation, including signal conditioning and selection of informative input signals. * Master the principles of parameter estimation using techniques like the Least Squares method for fitting model structures to data. * Practice defining and selecting appropriate model structures, such as ARX, ARMAX, and Box-Jenkins models. * Configure foundational model validation tests, including residual analysis and cross-validation, to ensure model accuracy and reliability. The course starts by defining dynamic systems and essential terminology. You will then progress through the stages of the identification process: data acquisition, model structure selection, parameter estimation, and rigorous model validation. This course is designed for absolute beginners in control engineering, data science, or systems analysis who need to learn how to create mathematical models from experimental data. No prior knowledge of system identification is required. Start building robust predictive system models today.