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vol 67 / October, 2024
Article

DOI 10.17586/0021-3454-2015-58-11-920-926

UDC 004.001;004.001.57;004.7.001.57

FORECASTING MODEL FOR MULTI-PARAMETER TECHNICAL SYSTEM STATE

A. V. Demin
ITMO University; Professor


S. P. Dmitrieva
ITMO University, Department of Optical and Digital Systems and Technologies; Research Engineer


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Abstract. An analytical model is developed for multi-parameter technical system state assessment. The model allows monitoring of the system objective functions during its operation on the base of a posteriori information. The model construction calls for identification of the system basic features and characteristic peculiarities, as well as limitations presented at the structural level as a set of domains; the requirements make it possible correct the system parameters ensuring their invariance with respect to disturbances. Application of the analytical model to assessment of the state of a multi-parameter technical system considered in relation to complex hierarchical distributed multi-agent dynamic measuring systems to be designed in the framework of the project on construction of weather station complex.
Keywords: computer modeling, analytical model, prediction, algorithm, evaluation, energy domain, complex hierarchical multi-agent dynamic measuring system

References:
  1. Ahrens C.D. Essentials of Meteorology: An Invitation to the Atmosphere, Cengage Learning, 2011, 506 p.
  2. Söffker D., Xingguang Fu, Hasselberg A., Langer M. Intern. Journal of Information Technology and Web Engineering, 2012, no. 7, 121 p.
  3. Livingstone D.A. Practical Guide to Scientific Data Analysis, John Wiley&Sons, 2010, 358 p.
  4. Demin A.V., Koporskiy N.S. Imitatsionnoe modelirovanie informatsionno-izmeritel'nykh i upravlyayushchikh sistem (Simulation Modeling of Information-Measuring and Control Systems), St. Petersburg, 2007, 138 р. (in Russ.)
  5. Arteta J., Marécal V., Rivière E.D. Atmospheric Chemistry and Physics, 2009, no. 9 (18), pp. 7081–7100.
  6. Eusgeld I., Kröger W. Proc. of the 9th Intern. Conf. on Probabilistic Safety Assessment and Management, 2008, no. 1, pp. 484–491.
  7. Vidal D., Zou X., Uesaka T. Tappi Journal, 2003, no. 4 (2), pp. 3–8.
  8. Jiang Z., Liu S., Dougal R.A. Proc. of the Conf. “IEEE SOUTHEASTCON”, 2002, pр. 113–120.
  9. Raysin K., Rice J., Dorman E., Matheny S. Oceans Conf. Record (IEEE), 1999, no. 2, pp. 747–752.
  10. Proc. of the 33rd Annual Hawaii Intern. Conf. on System Siences, 2000, pp. 95.
  11. Fu Z.J., Zhou X.D., Chen Y.Q., Gong J.H., Peng F., Yan Z.D., Zhang T.L., Yang L.Z. Communications in Nonlinear Science and Numerical Simulation, 2015, no. 3 (20), pp. 832–845.
  12. Bonilla J., Dormido S., Cellier F.E. Communications in Nonlinear Science and Numerical Simulation, 2015, no. 3 (20), pp. 743–768.
  13. Demin A.V., Dmitrieva S.P. Trudy III Kongressa po intellektual'nym sistemam i informatsionnym tekhnologiyam „IS&IT 14“(Proc. of the Intern. Conf. on Artificial Intelligence and Systems), Moscow, 2014, no. 3, pp. 289–294. (in Russ.)
  14. Dmitrieva S.P. Proc. of the IV Intern. Sci. Conf. “Science, Technology and Higher Education”, 2014, no. 2, pp. 302–307, http://science-canada.com/. (in Russ.)
  15. Dmitrieva S.P. Proc. of the V Intern. Sci. Conf. “Science and Education”, 2014, no. 2, pp. 180–185, http://www.euscience.de/. (in Russ.)