6.435 System Identification

As taught in: Spring 2005

A diagram of a  model for noisy outputs.

A model for noisy outputs taken from the course lecture notes. From this model, you can derive very important relations in system identification. (Figure by MIT OpenCourseWare.)

Level:

Graduate

Instructors:

Prof. Munther Dahleh

Course Features

Course Highlights

This course features a complete set of lecture notes. The course also features homework assignments with solutions.

Course Description

This course is offered to graduates and includes topics such as mathematical models of systems from observations of their behavior; time series, state-space, and input-output models; model structures, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.