Publication detail
Fuzzy model optimization by a genetic algorithm
HAMMER, M.
Czech title
Fuzzy model optimalizovaný genetickým algoritmem.
English title
Fuzzy model optimization by a genetic algorithm
Type
conference paper
Language
en
Original abstract
The lifetime of the insulating system of electric rotary machines is strongly dependet upon electrical and thermal features of the insulating material used. The subject of the diagnostic is to specify the condition of insulation used. At present days, the most popular diagnostic tools are the methods of artifical inteligence, and one mothod is the fuzzy modeling. However, this tool has many variable parameters, and the resulting efect is dependent upon on the suitable setting. This paper is concentrated on the use of genetic algorithms for the optimization of variable parameters in the fuzzy model that is used as a diagnostic tool for winding insulation of electric rotary machines. In this case, the optimization of fuzzy model means the minimize the mean absolute error for the diagnostics of winding insulation. The first section of the paper describes the architecture and the setting of the generic algorithm, the fuzzy model optimization and the input output data. The second section presents the optimization curves, and the calculated values that were specifed by the genetic algorithm as the optimized ones are shown in tables. The use of the diagnostic tool to solve the problems investigated is assessed in tables. The optimization method,the genetic algorithm and the fuzzy model were programmed in Matlab 6 enviroment. Also, all simulations and the calculated values were obtained by means of this product.
Czech abstract
Článek se zabývá diagnostikou izolačního materiálu elektrických strojů.
English abstract
The lifetime of the insulating system of electric rotary machines is strongly dependet upon electrical and thermal features of the insulating material used. The subject of the diagnostic is to specify the condition of insulation used. At present days, the most popular diagnostic tools are the methods of artifical inteligence, and one mothod is the fuzzy modeling. However, this tool has many variable parameters, and the resulting efect is dependent upon on the suitable setting. This paper is concentrated on the use of genetic algorithms for the optimization of variable parameters in the fuzzy model that is used as a diagnostic tool for winding insulation of electric rotary machines. In this case, the optimization of fuzzy model means the minimize the mean absolute error for the diagnostics of winding insulation. The first section of the paper describes the architecture and the setting of the generic algorithm, the fuzzy model optimization and the input output data. The second section presents the optimization curves, and the calculated values that were specifed by the genetic algorithm as the optimized ones are shown in tables. The use of the diagnostic tool to solve the problems investigated is assessed in tables. The optimization method,the genetic algorithm and the fuzzy model were programmed in Matlab 6 enviroment. Also, all simulations and the calculated values were obtained by means of this product.
Keywords in Czech
fuzzy
Keywords in English
Fuzzy
Released
01.01.2003
Publisher
TECOS
Location
Slovenia
ISBN
961-90401-7-1
Book
ICIT 2003 4th Internationl conference on Industrial Tools
Pages count
4
BIBTEX
@inproceedings{BUT13332,
author="Miloš {Hammer},
title="Fuzzy model optimization by a genetic algorithm",
booktitle="ICIT 2003 4th Internationl conference on Industrial Tools",
year="2003",
month="January",
publisher="TECOS",
address="Slovenia",
isbn="961-90401-7-1"
}