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.
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.
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Course Credit: MIT
|Review of Linear Systems, Review of Stochastic Processes, Defining a General Framework||00:30:00|
|Introductory Examples for System 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|
|Submit Your Assignment||00:00:00|
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