Open AccessApplication of the variation operator in a genetic algorithm for the synthesis of fuzzy controllers
Author(s) -
I. V. Kulikova
Publication year - 2021
Publication title -
vestnik dagestanskogo gosudarstvennogo tehničeskogo universiteta. tehničeskie nauki
Language(s) - English
Resource type - Journals
eISSN - 2542-095X
pISSN - 2073-6185
DOI - 10.21822/2073-6185-2020-47-4-92-100
Subject(s) - controller (irrigation) , operator (biology) , fuzzy logic , control theory (sociology) , genetic algorithm , fuzzy control system , algorithm , set (abstract data type) , mathematics , variation (astronomy) , mutation , computer science , point (geometry) , variable (mathematics) , mathematical optimization , control (management) , artificial intelligence , mathematical analysis , biochemistry , chemistry , physics , geometry , repressor , biology , transcription factor , astrophysics , agronomy , gene , programming language
. Objective. This article studies the problem of increasing the efficiency of fuzzy controller synthesis in a control system using a genetic algorithm. The best parameters of the fuzzy controller are selected using the crossing-over and mutation operators in the genetic algorithm. The operation of the mutation operator can lead to the formation of an incorrect set of parameters, which complicates the procedure for synthesizing a fuzzy controller. Methods . Arrays of parameter sets of membership functions, conclusions, and rule weights that are included in the fuzzy controller are compiled using mathematical simulation. The mechanism of operation of single-point and two-point variation operators in the genetic algorithm is described by the simulation modeling. Results . Mathematical models of single-point and two-point variation operators for the genetic algorithm are proposed. The mechanism for changing the values of elements in the array of a set of parameters of a fuzzy controller with one input and output variable is presented. Conclusion . Replacing the mutation operator with the variation operator eliminates the formation of incorrect sets of parameters of the fuzzy controller in the control system.