Course Websites
GE 524 - Data-Based Systems Modeling
Last offered Fall 2015
Official Description
Identification and building of mathematical and computational models directly from data. Systems and model types, such as state-space and distributed-parameter; parametric estimation methods, such as regression and least-squares recent subspace identification methods; data preprocessing techniques; model validation methods. Assignment applications to a wide range of dynamical systems, including biological, electro-mechanical, and economic. Course Information: Prerequisite: GE 424 and IE 300.
Related Faculty
Documents
Course Description
Construction of mathematical and computational models directly from data, relying on methods from optimization, statistical data analysis and linear systems theory. Overview of different systems and model types, such as ARMAX, state-space and distributed-parameter models; parametric estimation methods, such as regression and least-squares methods, discussion of subspace identification methods; some discussion of data preprocessing techniques; model validation methods. Assignments will include data from applications covering a wide range of dynamical systems, including biological, electro-mechanical, and economic. Prerequisite: GE 424 and IE 300.
| Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|
| Data-Based Systems Modeling | A | 53234 | LCD | 4 | 1030 - 1150 | M W F | 260 Everitt Laboratory | Carolyn L Beck |