Automatic differential equations identification by self-configuring genetic programming algorithm
The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information n...
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| Published in: | IOP conference series. Materials Science and Engineering Vol. 734; no. 1; pp. 12093 - 12100 |
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| Format: | Journal Article |
| Language: | English |
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IOP Publishing
01.01.2020
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| ISSN: | 1757-8981, 1757-899X |
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| Abstract | The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated. |
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| AbstractList | The paper considers a reduction of differential equations identification problem to the symbolic regression task. The current approach allows automatic determining the structure of a differential equation via the usage of the self-configuring genetic programming algorithm. The a priori information needed is only the dynamic system initial point and the sample of input and output effects. The stability of the proposed approach to the presence of noise in the sample and the small amount of data is investigated. |
| Author | Karaseva, T S |
| Author_xml | – sequence: 1 givenname: T S surname: Karaseva fullname: Karaseva, T S email: tatyanakarasewa@yandex.ru organization: Reshetnev Siberian State University of Science and Technology 31 , Russian Federation |
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| CitedBy_id | crossref_primary_10_3390_a15100351 crossref_primary_10_1051_itmconf_20245904012 |
| Cites_doi | 10.1504/IJICA.2013.055931 10.1007/978-3-642-37213-1_49 10.1007/3-540-45355-5_26 10.1080/002077200291154 10.1007/978-3-642-30976-2_50 |
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| Copyright | Published under licence by IOP Publishing Ltd 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1088/1757-899X/734/1/012093 |
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| References | Passino (MSE_734_1_012093bib4) 1998 Karaseva (MSE_734_1_012093bib12) 2018 Boguslavskiy (MSE_734_1_012093bib2) 2006 Al-Duwaish (MSE_734_1_012093bib8) 2000; 31 Bakhvalov (MSE_734_1_012093bib13) 2007 Astreon (MSE_734_1_012093bib9) 1965; 2 Pashchenko (MSE_734_1_012093bib6) 2006 Semenkin (MSE_734_1_012093bib14) 2012; 7331 Koza (MSE_734_1_012093bib11) 1992 Medvedev (MSE_734_1_012093bib1) 1975 Burakov (MSE_734_1_012093bib5) 2013; 5 Jovanovic (MSE_734_1_012093bib3) 1997; 1 Niehaus (MSE_734_1_012093bib15) 2001; 2038 Reimer (MSE_734_1_012093bib7) 2010 Ryzhikov (MSE_734_1_012093bib18) 2013; 7824 Ryzhikov (MSE_734_1_012093bib10) 2016 Tanabe (MSE_734_1_012093bib16) 2013 Karaseva (MSE_734_1_012093bib17); 69 |
| References_xml | – start-page: 48 year: 1975 ident: MSE_734_1_012093bib1 – volume: 5 start-page: 152 year: 2013 ident: MSE_734_1_012093bib5 article-title: Solving variational and Cauchy problems with self-configuring genetic programming algorithm publication-title: International Journal of Innovative Computing and Applications doi: 10.1504/IJICA.2013.055931 – start-page: 217 year: 2018 ident: MSE_734_1_012093bib12 – start-page: 636 year: 2007 ident: MSE_734_1_012093bib13 – start-page: 71 year: 2013 ident: MSE_734_1_012093bib16 – volume: 69 start-page: 80 ident: MSE_734_1_012093bib17 article-title: Self-configuring genetic programming algorithm for a Cauchy problem and variational problem in symbolic form publication-title: Proceedings of the Institute for systems analysis Russian academy of sciences – start-page: 1608 year: 2010 ident: MSE_734_1_012093bib7 – volume: 7824 start-page: 477 year: 2013 ident: MSE_734_1_012093bib18 article-title: Evolutionary strategies algorithm based approaches for the linear dynamic system identification publication-title: Lecture Notes in Computer Science doi: 10.1007/978-3-642-37213-1_49 – start-page: 502 year: 1998 ident: MSE_734_1_012093bib4 – start-page: 168 year: 2016 ident: MSE_734_1_012093bib10 – volume: 2038 start-page: 325 year: 2001 ident: MSE_734_1_012093bib15 article-title: Adaption of Operator Probabilities in Genetic Programming publication-title: Lecture Notes in Computer Science doi: 10.1007/3-540-45355-5_26 – volume: 1 start-page: 525 year: 1997 ident: MSE_734_1_012093bib3 article-title: Identification of Dynamic System Using Neural Network publication-title: Architecture and Civil Engineering – volume: 31 start-page: 307 year: 2000 ident: MSE_734_1_012093bib8 article-title: A genetic approach to the identification of linear dynamical systems with static nonlinearities publication-title: International Journal of Systems Science doi: 10.1080/002077200291154 – start-page: 785 year: 1992 ident: MSE_734_1_012093bib11 – volume: 2 start-page: 65 year: 1965 ident: MSE_734_1_012093bib9 article-title: Numerical Identification of Linear Dynamic Systems from Normal Operating Records publication-title: IFAC Proceedings Volumes – volume: 7331 start-page: 414 year: 2012 ident: MSE_734_1_012093bib14 article-title: Self-configuring Genetic Algorithm with Modified Uniform Crossover Operator publication-title: Lecture Notes in Computer Science doi: 10.1007/978-3-642-30976-2_50 – start-page: 208 year: 2006 ident: MSE_734_1_012093bib2 – start-page: 328 year: 2006 ident: MSE_734_1_012093bib6 |
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| SubjectTerms | Differential equations Genetic algorithms Mathematical analysis |
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| Title | Automatic differential equations identification by self-configuring genetic programming algorithm |
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