Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments

Standard

Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments. / Мусаев, Александр Азерович; Макшанов, Андрей; Григорьев, Дмитрий Алексеевич.

в: Mathematics, Том 10, № 11, 1797, 01.06.2022.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

BibTeX

@article{e3bc00d2971d4364a2c837686075a443,

title = "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments",

abstract = "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.",

keywords = "channel strategies, chaotic processes, control strategies, dynamic stability, non-stationary environment, numerical studies, observation series",

author = "Мусаев, {Александр Азерович} and Андрей Макшанов and Григорьев, {Дмитрий Алексеевич}",

note = "Publisher Copyright: {\textcopyright} 2022 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = jun,

day = "1",

doi = "10.3390/math10111797",

language = "English",

volume = "10",

journal = "Mathematics",

issn = "2227-7390",

publisher = "MDPI AG",

number = "11",

}

RIS

TY - JOUR

T1 - Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments

AU - Мусаев, Александр Азерович

AU - Макшанов, Андрей

AU - Григорьев, Дмитрий Алексеевич

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