Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

This study investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to...

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Main Author:	Ismail, H. Muh Yusuf
Format:	Thesis
Language:	English English
Published:	2010
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://eprints.utem.edu.my/id/eprint/13841/1/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller24_pages.pdf http://eprints.utem.edu.my/id/eprint/13841/2/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller.pdf
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id	my-utem-ep.13841
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spelling	my-utem-ep.138412015-05-28T04:34:32Z Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller 2010 Ismail, H. Muh Yusuf Q Science (General) QA Mathematics This study investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function and control rules generated by human operator. The membership function selection process is done with trial and error and it runs step by step which is too long in solving the underlined the problem. GA have been successfully applied to solve many optimization problems. This research proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems. The performance of GA can be further improved by using different combinations of selection strategies, crossover and mutation methods, and other genetic parameters such as population size, probability of crossover and mutation rate. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the method which proposed is very helpful to determine membership function and it is clear that the GA are very promising in improving the performance of the FLC to find the optimum result. 2010 Thesis http://eprints.utem.edu.my/id/eprint/13841/ http://eprints.utem.edu.my/id/eprint/13841/1/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller24_pages.pdf application/pdf en public http://eprints.utem.edu.my/id/eprint/13841/2/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller.pdf application/pdf en validuser http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000061103 masters UTeM Faculty of Information and Communication Technology
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Ismail, H. Muh Yusuf Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
description	This study investigates the use of Genetic Algorithms (GA) to design and implement of Fuzzy Logic Controllers (FLC). A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function and control rules generated by human operator. The membership function selection process is done with trial and error and it runs step by step which is too long in solving the underlined the problem. GA have been successfully applied to solve many optimization problems. This research proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems. The performance of GA can be further improved by using different combinations of selection strategies, crossover and mutation methods, and other genetic parameters such as population size, probability of crossover and mutation rate. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the method which proposed is very helpful to determine membership function and it is clear that the GA are very promising in improving the performance of the FLC to find the optimum result.
format	Thesis
qualification_level	Master's degree
author	Ismail, H. Muh Yusuf
author_facet	Ismail, H. Muh Yusuf
author_sort	Ismail, H. Muh Yusuf
title	Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
title_short	Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
title_full	Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
title_fullStr	Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
title_full_unstemmed	Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller
title_sort	genetic algorithms in optimizing membership function for fuzzy logic controller
granting_institution	UTeM
granting_department	Faculty of Information and Communication Technology
publishDate	2010
url	http://eprints.utem.edu.my/id/eprint/13841/1/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller24_pages.pdf http://eprints.utem.edu.my/id/eprint/13841/2/Genetic__Algorithms_in_optimizing_membership_funtion_for_fuzzy_logic_controller.pdf
_version_	1747833813884469248

Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

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