Title :
Identification of stochastic linear dynamic systems
Author_Institution :
Wolf Management Services, Palo Alto, California
Abstract :
Using a new form of representation, a set of equations has been derived for the maximum likelihood identification of linear dynamic systems. These equations are shown to be directly related to the filtering and smoothing equations for linear dynamic systems. Numerical results are obtained for a fourth order system using Davidon´s Conjugate Gradient Method which also gives the variances of the estimates.
Keywords :
Econometrics; Equations; Filtering; Kalman filters; Maximum likelihood estimation; Nonlinear filters; State estimation; State-space methods; Stochastic systems; White noise;
Conference_Titel :
Adaptive Processes (8th) Decision and Control, 1969 IEEE Symposium on
Conference_Location :
University Park, PA, USA
DOI :
10.1109/SAP.1969.269932