scholarly journals Identification algorithm for telecommunication systems with uncertain parameters of their vector of state stochastic model

2021 ◽  
Vol 2131 (2) ◽  
pp. 022090
Author(s):  
E G Chub ◽  
V A Pogorelov

Abstract The described method of structure identification of the state vector of a telecommunication system stochastic model is based on a posteriori probability density approximation (APDA) by a system of a posteriori moments. An assumption of possible APDA approximation by a class of Pearson distributions resulted in a closed system of moment equations. Implementation of optimal non-linear stochastic object control techniques helped to solve the problem of structural identification. Introduction of the proposed approach into contemporary telecommunication systems will not impose additional requirements on the calculating equipment, thus making this method well-suited for a wide range of applications.

Author(s):  
Stephen A. Andrews ◽  
Andrew M. Fraser

This paper reports a verification study for a method that fits functions to sets of data from several experiments simultaneously. The method finds a maximum a posteriori probability estimate of a function subject to constraints (e.g., convexity in the study), uncertainty about the estimate, and a quantitative characterization of how data from each experiment constrains that uncertainty. While this work focuses on a model of the equation of state (EOS) of gasses produced by detonating a high explosive, the method can be applied to a wide range of physics processes with either parametric or semiparametric models. As a verification exercise, a reference EOS is used and artificial experimental data sets are created using numerical integration of ordinary differential equations and pseudo-random noise. The method yields an estimate of the EOS that is close to the reference and identifies how each experiment most constrains the result.


2020 ◽  
Vol 224 ◽  
pp. 02029
Author(s):  
V Pogorelov ◽  
E Chub

A stochastic model of a nonadjustable data measurement complex platform for track geometry cars is introduced. A state vector evaluation algorithm based on the approximation of a posteriori probability density by the system of a posteriori moments is also offered.


1999 ◽  
Vol 105 (2) ◽  
pp. 1365-1366
Author(s):  
Thomas J. Green ◽  
William H. Payne ◽  
Vivian E. Titus ◽  
Eric J. Van Allen

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