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System Identification

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System Identification

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, parametrization, and identifiability; non-parametric methods; prediction error methods for parameter estimation, convergence, consistency, and asymptotic distribution; relations to maximum likelihood estimation; recursive estimation; relation to Kalman filters; structure determination; order estimation; Akaike criterion; bounded but unknown noise model; and robustness and practical issues.

Tags: Before 1300: Ancient and Medieval HistoryInforInformation environmentsInformation TheoryNutrition
Details: http://ocw.mit.edu/courses/e...m-identification-spring-2005
Categories: Computer Sciences
Starts : 2005-02-01

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