Tuning of fuzzy logic controllers by parameter estimation method

Fuzzy logic controllers (FLC) require fine tuning to match the rules to the membership functions or vice-versa. For the class of FLCs that mimic human process operators, the rule-membership function mismatch arises from the lack of information on the specifications of the membership functions. The r...

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Bibliographic Details
Main Authors:	Alang Md Rashid, Nahrul Khair, Heger, A. Sharif
Format:	Book Chapter
Language:	English English English English
Published:	Prentice Hall 1993
Subjects:	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/1294/ http://irep.iium.edu.my/1294/1/ch18 http://irep.iium.edu.my/1294/2/Book_cover.pdf http://irep.iium.edu.my/1294/3/Copyright_page.pdf http://irep.iium.edu.my/1294/4/Chapter_first_page.pdf

id	iium-1294
recordtype	eprints
spelling	iium-12942017-08-09T02:09:12Z http://irep.iium.edu.my/1294/ Tuning of fuzzy logic controllers by parameter estimation method Alang Md Rashid, Nahrul Khair Heger, A. Sharif TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Fuzzy logic controllers (FLC) require fine tuning to match the rules to the membership functions or vice-versa. For the class of FLCs that mimic human process operators, the rule-membership function mismatch arises from the lack of information on the specifications of the membership functions. The rules that are incorporated into the FLC knowledge base are broad generalizations of the operators’ control strategy. While the rules are readily available from the operator, the specifications of the membership functions are harder to define. For the class of FLCs that are used to control a process in which the control actions are not known a-priori, the rules and membership functions are derived using heuristics or based on the dynamics of the process that are obtained using simulation models. In this class of FLCs, and the one mentioned earlier, overlaps between variables fuzzy subsets, the slopes, and the functions used in defining the membership values all tend to dilute the generality of the rules and introduce specifics to the FLC. Prentice Hall 1993 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/1294/1/ch18 application/pdf en http://irep.iium.edu.my/1294/2/Book_cover.pdf application/pdf en http://irep.iium.edu.my/1294/3/Copyright_page.pdf application/pdf en http://irep.iium.edu.my/1294/4/Chapter_first_page.pdf Alang Md Rashid, Nahrul Khair and Heger, A. Sharif (1993) Tuning of fuzzy logic controllers by parameter estimation method. In: Fuzzy Logic and Control: Software and Hardware Applications. Environmental and Intelligent Manufacturing Systems Series, 2 . Prentice Hall, USA, pp. 374-392. ISBN 0-13-334251-4
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English English
topic	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering
spellingShingle	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices TK7885 Computer engineering Alang Md Rashid, Nahrul Khair Heger, A. Sharif Tuning of fuzzy logic controllers by parameter estimation method
description	Fuzzy logic controllers (FLC) require fine tuning to match the rules to the membership functions or vice-versa. For the class of FLCs that mimic human process operators, the rule-membership function mismatch arises from the lack of information on the specifications of the membership functions. The rules that are incorporated into the FLC knowledge base are broad generalizations of the operators’ control strategy. While the rules are readily available from the operator, the specifications of the membership functions are harder to define. For the class of FLCs that are used to control a process in which the control actions are not known a-priori, the rules and membership functions are derived using heuristics or based on the dynamics of the process that are obtained using simulation models. In this class of FLCs, and the one mentioned earlier, overlaps between variables fuzzy subsets, the slopes, and the functions used in defining the membership values all tend to dilute the generality of the rules and introduce specifics to the FLC.
format	Book Chapter
author	Alang Md Rashid, Nahrul Khair Heger, A. Sharif
author_facet	Alang Md Rashid, Nahrul Khair Heger, A. Sharif
author_sort	Alang Md Rashid, Nahrul Khair
title	Tuning of fuzzy logic controllers by parameter estimation method
title_short	Tuning of fuzzy logic controllers by parameter estimation method
title_full	Tuning of fuzzy logic controllers by parameter estimation method
title_fullStr	Tuning of fuzzy logic controllers by parameter estimation method
title_full_unstemmed	Tuning of fuzzy logic controllers by parameter estimation method
title_sort	tuning of fuzzy logic controllers by parameter estimation method
publisher	Prentice Hall
publishDate	1993
url	http://irep.iium.edu.my/1294/ http://irep.iium.edu.my/1294/1/ch18 http://irep.iium.edu.my/1294/2/Book_cover.pdf http://irep.iium.edu.my/1294/3/Copyright_page.pdf http://irep.iium.edu.my/1294/4/Chapter_first_page.pdf
first_indexed	2023-09-18T20:08:32Z
last_indexed	2023-09-18T20:08:32Z
_version_	1777407321782091776

Tuning of fuzzy logic controllers by parameter estimation method

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