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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v48.i9.50
pages 64-74

Synthesis Method of Empirical Models Optimal by Complexity under Uncertainty Conditions

Mikhail I. Gorbiychuk
Ivano-Frankovsk National Technical University of Oil and Gas, Ivano-Frankovsk
Taras V. Humenyuk
Ivano-Frankovsk National Technical University of Oil and Gas, Ivano-Frankovsk

ABSTRAKT

There was developed the synthesis method of optimal complexity models for conditions when the model variables are fuzzy values. The method is oriented to the class of polynomial models. The best models are selected by using criteria of regularity or displacement. The application of ideas of genetic algorithms gives the opportunity to eliminate the problem of large dimension which is characteristic of combinatorial method. The efficiency of the developed method was verified on industrial data that allowed one to synthesize the empirical model optimal by structure for drilling conditions.

REFERENZEN

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  3. Gorbiychuk M.I., Medvedchuk V.M., Lazoriv A.N., Analysis of parallel algorithm of empirical models synthesis on principles of genetic algorithms, Mezhdunarodnyi nauchno-tekhnicheskiy zhurnal “Problemy upravleniya i informatiki”, 2016, No. 1, 112-130.

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