Journal of Applied Mathematics and Stochastic Analysis
Volume 8 (1995), Issue 3, Pages 249-260
doi:10.1155/S1048953395000220
Identification of linear stochastic systems based on partial
information
1University of Ottawa, Department of Electrical Engineering and Department of Mathematic, Ontario, Ottawa, Canada
2University of Ottawa, Department of Electrical Engineering, Ontario, Ottawa, Canada
Received 1 October 1994; Revised 1 May 1995
Copyright © 1995 N. U. Ahmed and S. M. Radaideh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In this paper, we consider an identification problem for a system of partially
observed linear stochastic differential equations. We present a result whereby one
can determine all the system parameters including the covariance matrices of the
noise processes. We formulate the original identification problem as a deterministic control problem and prove the equivalence of the two problems. The method
of simulated annealing is used to develop a computational algorithm for identifying the unknown parameters from the available observation. The procedure is
then illustrated by some examples.