On the Operational Significance of the Securable Subspace for Partially Observed Linear Stochastic Systems

2018 ◽  
Author(s):  
Bharadwaj Satchidanandan ◽  
P. R. Kumar
Keyword(s):  
Stochastic Systems ◽  
Partially Observed ◽  
Linear Stochastic Systems

scholarly journals Identification of linear stochastic systems based on partial information

1995 ◽  
Vol 8 (3) ◽  
pp. 249-260 ◽  
Author(s):  
N. U. Ahmed ◽  
S. M. Radaideh
Keyword(s):  
Stochastic Systems ◽  
Computational Algorithm ◽  
Partial Information ◽  
Identification Problem ◽  
Covariance Matrices ◽  
Unknown Parameters ◽  
System Parameters ◽  
Partially Observed ◽  
Linear Stochastic Systems ◽  
Deterministic Control

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.

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