Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254667" target="_blank" >RIV/61989100:27240/23:10254667 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27740/23:10254667

Result on the web

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0960077923002783" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0960077923002783</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.chaos.2023.113377" target="_blank" >10.1016/j.chaos.2023.113377</a>

Result language

angličtina

Original language name

Forecasting and stabilizing chaotic regimes in two macroeconomic models via artificial intelligence technologies and control methods

Original language description

One of the key tasks in the economy is forecasting the economic agents&apos; expectations of the future values of economic variables using mathematical models. The behavior of mathematical models can be irregular, including chaotic, which reduces their predictive power. In this paper, we study the regimes of behavior of two economic models and identify irregular dynamics in them. Using these models as an example, we demonstrate the effectiveness of evolutionary algorithms and the continuous deep Q-learning method in combination with Pyragas control method for deriving a control action that stabilizes unstable periodic trajectories and suppresses chaotic dynamics. We compare qualitative and quantitative characteristics of the model&apos;s dynamics before and after applying control and verify the obtained results by numerical simulation. Proposed approach can improve the reliability of forecasting and tuning of the economic mechanism to achieve maximum decision-making efficiency.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Chaos, Solitons &amp; Fractals

ISSN

0960-0779

e-ISSN

1873-2887

Volume of the periodical

170

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

7

Pages from-to

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UT code for WoS article

001030254100001

EID of the result in the Scopus database

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