| Form of presentation | Articles in international journals and collections |
| Year of publication | 2019 |
| Язык | английский |
|
Mokshin Anatoliy Vasilevich, author
|
| Bibliographic description in the original language |
Mokshin A. V. Adaptive genetic algorithms used to analyze behavior of complex system / A. V. Mokshin, V. V. Mokshin, L. M. Sharnin // Communications in Nonlinear Science and Numerical Simulation. - 2019. - Vol. 71. - P. 174-186. |
| Annotation |
In the present study, we consider a complex system whose behavior is characterized by set of various time-dependent factors. Some of these factors can characterize the external influences on the system, whereas other factors contain information generated by system. We demonstrate that time-dependence of these factors can be reproduced by the nonlinear regression model. The concrete form of this regression model is constructed on the basis of the genetic algorithms technique. This allows us to predict a possible behavior of the system and to identify the so-called significant factors that have a significant impact on the behavior of the system. To demonstrate validity of the method, we apply it to analyze the data characterizing a manufacturing company and the meteorological data. |
| Keywords |
complex system, regression analysis, machine-learning, genetic algorithms |
| The name of the journal |
Communications in Nonlinear Science and Numerical Simulation
|
| URL |
https://doi.org/10.1016/j.cnsns.2018.11.014 |
| Please use this ID to quote from or refer to the card |
https://repository.kpfu.ru/eng/?p_id=189849&p_lang=2 |
Full metadata record  |
| Field DC |
Value |
Language |
| dc.contributor.author |
Mokshin Anatoliy Vasilevich |
ru_RU |
| dc.date.accessioned |
2019-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2019-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2019 |
ru_RU |
| dc.identifier.citation |
Mokshin A. V. Adaptive genetic algorithms used to analyze behavior of complex system / A. V. Mokshin, V. V. Mokshin, L. M. Sharnin // Communications in Nonlinear Science and Numerical Simulation. - 2019. - Vol. 71. - P. 174-186. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/eng/?p_id=189849&p_lang=2 |
ru_RU |
| dc.description.abstract |
Communications in Nonlinear Science and Numerical Simulation |
ru_RU |
| dc.description.abstract |
In the present study, we consider a complex system whose behavior is characterized by set of various time-dependent factors. Some of these factors can characterize the external influences on the system, whereas other factors contain information generated by system. We demonstrate that time-dependence of these factors can be reproduced by the nonlinear regression model. The concrete form of this regression model is constructed on the basis of the genetic algorithms technique. This allows us to predict a possible behavior of the system and to identify the so-called significant factors that have a significant impact on the behavior of the system. To demonstrate validity of the method, we apply it to analyze the data characterizing a manufacturing company and the meteorological data. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
complex system |
ru_RU |
| dc.subject |
regression analysis |
ru_RU |
| dc.subject |
machine-learning |
ru_RU |
| dc.subject |
genetic algorithms |
ru_RU |
| dc.title |
Adaptive genetic algorithms used to analyze behavior of complex system |
ru_RU |
| dc.type |
Articles in international journals and collections |
ru_RU |
|
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