Symbolic regression methods for control system synthesis

In this paper we use symbolic regression methods for control system synthesis. We compare three methods: network operator method, genetic programming and analytical programming. We developed variational versions of genetic programming and analytical programming to improve the search process efficiency. All the methods perform search over the set of the small variations of the given basic solution. Search efficiency depends on the basic solution. We give an example of control system synthesis for the unmanned vehicle with the state constraints over the set of the initial states using these methods. © 2014 IEEE.

Авторы
Редакторы
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Сборник материалов конференции
Издательство
Institute of Electrical and Electronics Engineers Inc.
Номер выпуска
-
Язык
Английский
Страницы
587-592
Статус
Опубликовано
Подразделение
-
Ссылка
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Номер
6961436
Том
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Год
2014
Организации
  • 1 Dorodnicyn Computing Centre of RAS, Institution of Russian Academy of Sciences, Moscow, Russian Federation
  • 2 Cybernetics and Mechatronics Dept., Peoples' Friendship Univ. of Russia, Moscow, Russian Federation
Ключевые слова
Control systems; Genetic algorithms; Genetic programming; Regression analysis; Basic solutions; Initial state; Network operator; Search efficiency; Search process; Small variations; State constraints; Symbolic regression; Control system synthesis
Дата создания
19.10.2018
Дата изменения
19.10.2018
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/5015/