MIT OpenCourseWare

Browse materialsMIT 6.435 · Spring 2005

System Identification

Subjects: Engineering · Mathematics · Science

Art: Palmer, Edward, 1803-1872, Figures 113-123. Plate Electrical Machines, 1840 · Public domain

Instructor: Prof. Munther Dahleh
Term: Spring 2005
Level: Graduate
What's inside: 1 lecture note · 20 other files · 3 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 6.435 System Identification, Spring 2005, Prof. Munther Dahleh — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Prerequisites are not listed here. Check MIT's own syllabus for this course.

How this course was organized

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Lecture Notes

12 files

Assignments

8 files

MIT OpenCourseWare, Prof. Munther Dahleh, 6.435 System Identification, Spring 2005.

License: CC BY-NC-SA 4.0 · Edition: Spring 2005 · Open the original course on OCW ↗

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System Identification

Before you start

What's here, and what isn't

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Materials by section

Lecture Notes

Assignments

Lecture 1: Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework

Lecture 2: Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework (cont.)

Lecture 3: Introductory Examples for System Identification

Lecture 4: Introductory Examples for System Identification (cont.)

Lecture 5: Nonparametric Identification

Lecture 6: Nonparametric Identification (cont.)

Lecture 7: Input Design, Persistence of Excitation, Pseudo-random Sequences

Lecture 8: Input Design, Persistence of Excitation, Pseudo-random Sequences (cont.)

Lecture 9: Least Squares, Statistical Properties

Lecture 10: Least Squares, Statistical Properties (cont.)

Lecture 11: Parametrized Model Structures, One-step Predictor, Identifiability

Lecture 12: Parametrized Model Structures, One-step Predictor, Identifiability (cont.)

Lecture 13: Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood

Lecture 14: Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood (cont.)

Lecture 15: Convergence and Consistency, Informative Data, Convergence to the True Parameters

Lecture 16: Convergence and Consistency, Informative Data, Convergence to the True parameters (cont.)

Lecture 17: Asymptotic Distribution of PEM

Lecture 18: Asymptotic Distribution of PEM (cont.)

Lecture 19: Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results

Lecture 20: Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results (cont.)

Lecture 21: Computation, Levinson Algorithm, Recursive Estimation

Lecture 22: Computation, Levinson Algorithm, Recursive Estimation (cont.)

Lecture 23: Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples

Lecture 24: Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples (cont.)