EE Department Intranet -
Close window CTRL+W

ELEC97085 (EE4-27) Systems Identification and Learning

Lecturer(s): Prof Thomas Parisini


The aim of the course is to introduce methods for constructing stochastic models of dynamic systems from measurements of input and output signals, and basic techniques for prediction of unknown quantities basing on available sensor data.

Learning Outcomes

This course will equip students with techniques for describing dynamic systems by stochastic models, for estimating parameters in these models from input/output data and to design prediction algorithms making use of data provided by sensors, as well as historical data. Students will also gain an understanding of simple techniques for assessing the quality of parameter estimates in these models.


Stochastic dynamical systems and their description in terms of standard discrete time models; these include, for example, linear difference equation models known as ARMAX (Autoregressive Moving Average models with Exogenous input) models. Least-squares parameter estimation. The statistical properties of parameter estimates. Recursive algorithms for parameter identification. Sources of parameter bias. The effects of over-parameterization. Techniques for selecting model order.

Exam Duration: 3:00hrs
Coursework contribution: 0%

Term: Autumn

Closed or Open Book (end of year exam): Closed

Coursework Requirement:

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

Course Homepage: unavailable

Book List:
Please see Module Reading list