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Machine Learning-Based Cyber-Physical Systems for Forecasting Short-Term State of Unstable Systems

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Part of the Studies in Systems, Decision and Control book series (SSDC,volume 417)

Abstract

We consider the task of developing algorithms for cyber-physical systems (CPS) for proactively managing the state of unstable systems with a chaotically evolving state vector. Examples of such processes are changes in the state of gas- and hydrodynamic environments, stock price evolution, thermal phenomena, and so on. The main problem of this type of CPS is creating forecasts that would allow us to compare the efficiency of different feasible control actions. The presence of a chaotic element in the state dynamics of unstable systems does not allow to build of control CPS based on conventional statistical extrapolation algorithms. Hence, in the current chapter, we consider forecasting algorithms built upon machine learning and instance-based data analysis. In the conditions of chaotic influences, which are common in unstable immersion environments, obtaining an accurate forecast is highly complicated. Within the conducted computational experiment that employed direct averaging by three after-effects of analog windows, the average forecast accuracy oscillates between 15 and 20%. Effective forecasting of a chaotic process of a complicated inertia-less nature based on the considered computational schemes has not been achieved yet. This means that additional research, based on multidimensional statistical measures, is required.

Keywords

  • Precedent forecasting
  • Controlling of a chaotic system
  • Matrix similarity measures

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Acknowledgements

The research of Alexander Musaev described in this paper is partially supported is partially supported by the Russian Foundation for Basic Research (grant 20-08-01046), state research FFZF-2022-0004. Dmitry Grigoriev research for this paper was funded by a Support from The Endowment Fund of St Petersburg University. The authors are grateful to participants at the Center for Econometrics and Business Analytics (CEBA, St. Petersburg University) seminar series for helpful comments and suggestions.

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Musaev, A., Grigoriev, D. (2022). Machine Learning-Based Cyber-Physical Systems for Forecasting Short-Term State of Unstable Systems. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M. (eds) Cyber-Physical Systems: Intelligent Models and Algorithms. Studies in Systems, Decision and Control, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-95116-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-95116-0_16

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