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Inform. Primen., 2013, Volume 7, Issue 1, Pages 44–53 (Mi ia243)  

This article is cited in 2 scientific papers (total in 2 papers)

Algorithms for inductive generation of superpositions for approximation of experimental data

G. I. Rudoya, V. V. Strijovb

a Moscow Institute of Physics and Technology
b Dorodnicyn Computing Centre of RAS

Abstract: The paper presents an algorithm which inductively generates admissible nonlinear models. An algorithm to generate all admissible superpositions of given complexity in finite number of iterations is proposed. The proof of its correctness is stated. The proposed approach is illustrated by a computational experiment on synthetic data.

Keywords: symbolic regression; nonlinear models; inductive generation; models complexity.

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Citation: G. I. Rudoy, V. V. Strijov, “Algorithms for inductive generation of superpositions for approximation of experimental data”, Inform. Primen., 7:1 (2013), 44–53

Citation in format AMSBIB
\Bibitem{RudStr13}
\by G.~I.~Rudoy, V.~V.~Strijov
\paper Algorithms for inductive generation of~superpositions for~approximation of~experimental data
\jour Inform. Primen.
\yr 2013
\vol 7
\issue 1
\pages 44--53
\mathnet{http://mi.mathnet.ru/ia243}


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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. G. I. Rudoy, “On applying Monte Carlo methods to analysis of nonlinear regression models”, Num. Anal. Appl., 8:4 (2015), 344–350  mathnet  crossref  crossref  mathscinet  elib  elib
    2. A. M. Bochkarev, I. L. Sofronov, V. V. Strizhov, “Porozhdenie ekspertno-interpretiruemykh modelei dlya prognoza pronitsaemosti gornoi porody”, Sistemy i sredstva inform., 27:3 (2017), 74–87  mathnet  crossref  elib
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