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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. Topics such as parameterization, and identifiability, non-parametric methods, prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution, structure determination, order estimation, bounded but unknown noise model, and robustness and practical issues will be discussed in this course.

Assessment

This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.

Certification

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Course Credit: MIT

Course Curriculum

Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework 00:30:00
Introductory Examples for System Identification 00:40:00
Nonparametric Identification 00:40:00
Input Design, Persistence of Excitation, Pseudo-random Sequences 00:25:00
Least Squares, Statistical Properties 00:10:00
Parametrized Model Structures, One-step Predictor, Identifiability 00:40:00
Parameter Estimation Methods, Minimum Prediction Error Paradigm, Maximum Likelihood 00:20:00
Convergence and Consistency, Informative Data, Convergence to the True Parameters 00:35:00
Asymptotic Distribution of PEM 00:15:00
Instrumental Variable Methods, Identification in Closed Loop, Asymptotic Results 00:15:00
Computation, Levinson Algorithm, Recursive Estimation 00:30:00
Identification in Practice, Error Filtering, Order Estimation, Model Structure Validation, Examples 01:20:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

Course Reviews

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