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Lecture Notes - MIT - System Identification PDF

70 Pages·2014·English
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by ['sabrahima']| 2014| 70 pages| English

About Lecture Notes - MIT - System Identification

This document summarizes key points from a lecture on practical system identification. It discusses pre-treating data by removing biases, filtering high-frequency noise, and handling outliers. It also covers choosing appropriate model structures based on flexibility, parsimony, and validating models with new data. Order estimation methods are examined, including using covariance matrices and residual whiteness tests. Examples are provided comparing identification for different experiments and signal-to-noise ratios. In general, noise can help identification asymptotically but hinders it with finite data by obscuring system excitation.

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Author:['sabrahima']
Publication Year:2014
Pages:70
Language:English
Format:PDF
Price:FREE
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