Research output: Contribution to journal › Article › peer-review
Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments. / Мусаев, Александр Азерович; Макшанов, Андрей; Григорьев, Дмитрий Алексеевич.
In: Mathematics, Vol. 10, No. 11, 1797, 01.06.2022.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments
AU - Мусаев, Александр Азерович
AU - Макшанов, Андрей
AU - Григорьев, Дмитрий Алексеевич
N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model.
AB - We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model.
KW - channel strategies
KW - chaotic processes
KW - control strategies
KW - dynamic stability
KW - non-stationary environment
KW - numerical studies
KW - observation series
UR - https://www.mendeley.com/catalogue/844322b2-12e3-3e20-9f23-cb8432a6dd7d/
UR - http://www.scopus.com/inward/record.url?scp=85131569418&partnerID=8YFLogxK
U2 - 10.3390/math10111797
DO - 10.3390/math10111797
M3 - Article
VL - 10
JO - Mathematics
JF - Mathematics
SN - 2227-7390
IS - 11
M1 - 1797
ER -
ID: 95172554