6.435 System Identification
Mathematical models of systems from observations of their behavior. Time series, state-space, and input-output models. Model structures, parametrization, and identifiability. Nonparametric 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.
This class has 6.241 as a prerequisite.
6.435 will not be offered this semester. It will be instructed by M. A. Dahleh.
This class counts for a total of 12 credits. This is a graduate-level class.
You can find more information at the 6.435 - Theory of Learning and System Identification - Spring 2007 site.
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