M. Rashidifar, and A. abertavi. International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE), 2 (2):
01-12(May 2014)
Abstract
Neural networks and genetic algorithms have been in the past successfully applied, separately, to
controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear
mapping capabilities of neural networks to model objective functions, and use them to supply their values
to a genetic algorithm which performs on-line minimization. Simulation results show that this is a valid
approach, offering desired properties for on-line use such as a dramatic reduction in computation time and
avoiding the need of perturbing the closed-loop operation.
%0 Journal Article
%1 noauthororeditor
%A Rashidifar, Mohammad Amin
%A abertavi, Abdolah
%D 2014
%J International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE)
%K Algorithms Autotuning Genetic Networks Neural PID
%N 2
%P 01-12
%T A NOVEL TECHNIQUE FOR CONTROLLER
TUNING
%U http://airccse.com/ijaceee/papers/2214ijaceee01.pdf
%V 2
%X Neural networks and genetic algorithms have been in the past successfully applied, separately, to
controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear
mapping capabilities of neural networks to model objective functions, and use them to supply their values
to a genetic algorithm which performs on-line minimization. Simulation results show that this is a valid
approach, offering desired properties for on-line use such as a dramatic reduction in computation time and
avoiding the need of perturbing the closed-loop operation.
@article{noauthororeditor,
abstract = {Neural networks and genetic algorithms have been in the past successfully applied, separately, to
controller tuning problems. In this paper we purpose to combine its joint use, by exploiting the nonlinear
mapping capabilities of neural networks to model objective functions, and use them to supply their values
to a genetic algorithm which performs on-line minimization. Simulation results show that this is a valid
approach, offering desired properties for on-line use such as a dramatic reduction in computation time and
avoiding the need of perturbing the closed-loop operation.
},
added-at = {2018-06-02T11:08:38.000+0200},
author = {Rashidifar, Mohammad Amin and abertavi, Abdolah},
biburl = {https://www.bibsonomy.org/bibtex/2d186e2c4ba4cb279a00792359728c879/electical12345},
interhash = {d675c1d0830c4e0113fff7bb95a5adca},
intrahash = {d186e2c4ba4cb279a00792359728c879},
issn = {2394 - 0816},
journal = {International Journal of Applied Control, Electrical and Electronics Engineering (IJACEEE) },
keywords = {Algorithms Autotuning Genetic Networks Neural PID},
language = {English},
month = may,
number = 2,
pages = {01-12},
timestamp = {2018-06-02T11:08:38.000+0200},
title = {A NOVEL TECHNIQUE FOR CONTROLLER
TUNING},
url = {http://airccse.com/ijaceee/papers/2214ijaceee01.pdf},
volume = 2,
year = 2014
}