Title :
Identification and model approximation for continuous-time systems on finite parameter sets
Author :
Tugnait, Jitendra K.
Author_Institution :
University of Iowa, Iowa City, IA, USA
fDate :
12/1/1980 12:00:00 AM
Abstract :
Almost-sure convergence of the maximum likelihood and the maximum a posteriori probability estimates of unknown parameters of continuous-time stochastic dynamical linear time-invariant systems is investigated. The unknown parameter set is assumed to be finite. The situation where the ture parameter does not belong to the unknown parameter set is considered, as well as the situation where the true model is included in the unknown parameter set.
Keywords :
Linear systems, stochastic continuous-time; MAP estimation; Parameter identification; maximum-likelihood (ML) estimation; Character generation; Convergence; Covariance matrix; Maximum likelihood estimation; Parameter estimation; Q measurement; Riccati equations; Stochastic systems; Sufficient conditions; Vectors;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1980.1102519
