scholarly journals Synthesis of Stochastic Systems with Partial Information via Control Barrier Functions

2020 ◽  
Vol 53 (2) ◽  
pp. 2441-2446
Author(s):  
Niloofar Jahanshahi ◽  
Pushpak Jagtap ◽  
Majid Zamani
Keyword(s):  
Stochastic Systems ◽  
Partial Information ◽  
Barrier Functions

scholarly journals Partial Information Stochastic Differential Games for Backward Stochastic Systems Driven by Lévy Processes

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Fu Zhang ◽  
QingXin Meng ◽  
MaoNing Tang
Keyword(s):  
Differential Game ◽  
Stochastic Systems ◽  
Partial Information ◽  
Backward Stochastic Differential Equation ◽  
Game Problem ◽  
Stochastic Differential Game ◽  
Linear Quadratic ◽  
Independent Brownian Motion ◽  
Teugels Martingales ◽  
Zero Sum

In this paper, we consider a partial information two-person zero-sum stochastic differential game problem, where the system is governed by a backward stochastic differential equation driven by Teugels martingales and an independent Brownian motion. A sufficient condition and a necessary one for the existence of the saddle point for the game are proved. As an application, a linear quadratic stochastic differential game problem is discussed.

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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