Introduction to modern methods of linear system identification. Different types of models. Review of classic time- and frequency-based approach to empirical, ‘black-box’ system modeling. Non-parametric identification: impulse and step weights, spectral analysis. Parametric, discrete transfer function models from I/O data using Least Squares. Data-collection procedures, model structure selection, use of auto- and cross-correlation functions for diagnostics and model validation, overview of different estimation algorithms. Lab work consists of Matlab tutorials and an assignment dealing with identification of an unknown process. Course evaluation includes a group project selected from a list of topics in control system application, and its class presentation. Lect: 3 hrs./Lab: 1 hr. Prerequisite: ELE 639 Course Weight: 1.00 Billing Units: 1
