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System Identification, Spring 2005

(Complete Item Description)

Abstract:: 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, andasymptotic distribution. Relations to maximum likelihood estimation. Recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; and bounded but unknown noise models. Robustness and practical issues.

Subject:: Science and Technology
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

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Top Keywords

Akaike Criterion Andasymptotic Distribution Bounded Noise Models Computer Sciences Consistency Convergence Electrical Engineering Identifiability Input-Output Models Kalman Filters Mathematical Models Maximum Likelihood Estimation Model Structures Non-Parametric Methods Order Estimation Parameter Estimation Parametrization Prediction Error Recursive Estimation Robustness State-Space Structure Determination Time Series

Conditions of Use

No Strings Attached
Remix and Share
Share Only
Read the Fine Print

No restrictions on your remixing, redistributing, or making derivative works. Give credit to the author, as required.

Your remixing, redistributing, or making derivatives works comes with some restrictions, including how it is shared.

Your redistributing comes with some restrictions. Do not remix or make derivative works.

Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.

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