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.
Identification of linear stochastic systems based on partial
information
