3 credits
Fall 2025 Lecture Distance Learning Upper Division
Theory and application of System Identification methods. Connecting the world of mathematical models to experimental data - least squares methods and difference equation models. Background in probability and analysis: algebra of random variables, law of large numbers, central limit theorem. The ARMA family of models; mapping physics models to generalized ARMAX forms (linear and nonlinear); mapping the parameter estimation problem to the least squares problem (batch and recursive), and numerical solution techniques. Model (in)validation, optimal identification criteria, experiment design and data preprocessing considerations. Issues of signal-to-noise ratio, persistency of excitation, sampling frequency, data accuracy and data sizes.
Learning Outcomes1Interpret data probabilistically and ability to calculate uncertainty propagation.
2Fit Blackbox models to data; convert physics models to identifiable forms for least squares formulations.
3Construction of numerical codes for solving least squares problems and use of MATLAB System ID toolbox.
4Construct informative experiments for dynamic systems with suitable precision of and accuracy of sensing, data length, persistency of excitation, and sampling frequency.
Restrictions
made by Purdue students.