FGA Temperature Control for Incubating Egg

This paper investigates the use of genetic algorithms (GA) in the design and implementation of fuzzy logic controllers (FLC) for incubating egg. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership f...

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Main Authors: Ismail Yusuf, Yusram Yusuf, Nur Iksan
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Advances in Fuzzy Systems
Online Access:http://dx.doi.org/10.1155/2012/506082
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author Ismail Yusuf
Yusram Yusuf
Nur Iksan
author_facet Ismail Yusuf
Yusram Yusuf
Nur Iksan
author_sort Ismail Yusuf
collection DOAJ
description This paper investigates the use of genetic algorithms (GA) in the design and implementation of fuzzy logic controllers (FLC) for incubating egg. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions, but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error, and it runs step by step which takes too long in solving the problem. This paper develops a system that may help users to determine the membership function of FLC using the GA optimization for the fastest processing in solving the problems. The data collection is based on the simulation results, and the results refer to the transient response specification which is maximum overshoot. From the results presented, we will get a better and exact result; the value of overshot is decreasing from 1.2800 for FLC without GA to 1.0081 with GA (FGA).
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series Advances in Fuzzy Systems
spelling doaj-art-e2cbfa34ef2b4793b9056e7242c141ee2025-08-20T02:21:14ZengWileyAdvances in Fuzzy Systems1687-71011687-711X2012-01-01201210.1155/2012/506082506082FGA Temperature Control for Incubating EggIsmail Yusuf0Yusram Yusuf1Nur Iksan2Department of Computer, Lamintang Education and Training (LET-Indonesia), Perum Bandaramas Blok D5 1, Kota Batam, Kepri 29464, IndonesiaDepartment Of Mechanical Enginering, Sekolah Menengah Kejuruan (SMK) 5 Makassar, Jalan, Sunu 162, Makassar, Sulawesi Selatan 90214, IndonesiaDepartment of Computer, Lamintang Education and Training (LET-Indonesia), Perum Bandaramas Blok D5 1, Kota Batam, Kepri 29464, IndonesiaThis paper investigates the use of genetic algorithms (GA) in the design and implementation of fuzzy logic controllers (FLC) for incubating egg. What is the best to determine the membership function is the first question that has been tackled. Thus it is important to select the accurate membership functions, but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error, and it runs step by step which takes too long in solving the problem. This paper develops a system that may help users to determine the membership function of FLC using the GA optimization for the fastest processing in solving the problems. The data collection is based on the simulation results, and the results refer to the transient response specification which is maximum overshoot. From the results presented, we will get a better and exact result; the value of overshot is decreasing from 1.2800 for FLC without GA to 1.0081 with GA (FGA).http://dx.doi.org/10.1155/2012/506082
spellingShingle Ismail Yusuf
Yusram Yusuf
Nur Iksan
FGA Temperature Control for Incubating Egg
Advances in Fuzzy Systems
title FGA Temperature Control for Incubating Egg
title_full FGA Temperature Control for Incubating Egg
title_fullStr FGA Temperature Control for Incubating Egg
title_full_unstemmed FGA Temperature Control for Incubating Egg
title_short FGA Temperature Control for Incubating Egg
title_sort fga temperature control for incubating egg
url http://dx.doi.org/10.1155/2012/506082
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