Spectral method of analysis and optimal estimation in linear stochastic systems

2020 ◽  
Vol 11 (03) ◽  
pp. 2050022 ◽  
Author(s):  
K. A. Rybakov
Keyword(s):  
Control Systems ◽  
Multiplicative Noise ◽  
Stochastic Systems ◽  
Process Analysis ◽  
Optimal Estimation ◽  
Output Process ◽  
Spectral Form ◽  
Analysis Problem ◽  
Markov Random ◽  
Linear Stochastic Systems

It is proposed to use the spectral form of mathematical description of control systems for modeling continuous-time Markov random processes described by linear stochastic differential equations with additive or multiplicative noise. The obtained results are applied to solve the output process analysis problem and the optimal estimation problem.

New Results on Optimal Joint Parameter and State Estimation of Linear Stochastic Systems

1980 ◽  
Vol 102 (1) ◽  
pp. 28-34 ◽  
Author(s):  
G. Salut ◽  
J. Aguilar-Martin ◽  
S. Lefebvre
Keyword(s):  
Stochastic Systems ◽  
Optimal Estimation ◽  
Canonical Representation ◽  
Parameters Estimation ◽  
Space Representation ◽  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
State Space Representation ◽  
Classical State ◽  
Linear Stochastic Systems

In this paper a complete presentation of a new canonical representation of multiinput, multioutput linear stochastic systems is given. Its equivalence with operator form directly linked with ARMA processes as well as with classical state space representation is given, and a transfer matrix interpretation is developed in an example. The importance of the new representation is mainly in the fact that in the joint state and parameters estimation problem, all unknown parameters appear linearly when an input-output record is available. Moreover, if noises are Gaussian and their statistics are known, a conditionally time varying Kalman-Bucy type filter gives the recursive optimal estimation of parameters and state. Historical comments and remarks about the adaptive version of this algorithm are given. Finally an illustrative low order example is described.

The effect of pressure on the compressibility of aluminum alloys

2020 ◽  
pp. 102-107
Keyword(s):  
Mechanical Properties ◽  
Mathematical Model ◽  
Liquid Metal ◽  
Control Systems ◽  
Physical And Mechanical Properties ◽  
Reliable Data ◽  
Output Process ◽  
Automated Control ◽  
Input And Output ◽  
Crystallization Under Pressure

To obtain reliable data on the properties of liquid metal and create automated control systems, the technological process of molding with crystallization under pressure is studied. A mathematical model of the input and output process parameters is developed. It is established that the compressibility of the melt can represent the main controlled parameter influencing on the physical-mechanical properties of the final products. The obtained castings using this technology are not inferior in their physical and mechanical properties to those produced by forging or stamping.

The stochastic observability for noisy non-linear stochastic systems†

1975 ◽  
Vol 22 (4) ◽  
pp. 461-480 ◽  
Author(s):  
YOSHIFUMI SUNAHARA ◽  
SHIN'ICHl AIHARA ◽  
MASAYUKI SHIRAIWA
Keyword(s):  
Stochastic Systems ◽  
Linear Stochastic Systems ◽  
Non Linear
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