Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules

The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and nor...

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Main Authors:	Teh, Chin Ying, Tay, Kai Meng, Lim, Cheepeng
Format:	Book Section
Language:	English
Published:	Springer 2017
Subjects:	QA Mathematics
Online Access:	http://ir.unimas.my/15755/ http://ir.unimas.my/15755/ http://ir.unimas.my/15755/ http://ir.unimas.my/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf

id	unimas-15755
recordtype	eprints
spelling	unimas-157552017-04-12T02:55:27Z http://ir.unimas.my/15755/ Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng QA Mathematics The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and normal trapezoid fuzzy sets are used as fuzzy membership functions. Besides that, a strong fuzzy partition strategy is used. Our empirical analysis shows that a set of multi-attribute monotone data may lead to non-monotone fuzzy If-Then rules. The same observation can be made, empirically, using adaptive neuro-fuzzy inference system (ANFIS), a well-known and popular FIS model with neural learning capability. This finding is important for the modeling of a monotone FIS model, because it shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course of developing data-driven FIS models. Springer 2017 Book Section PeerReviewed text en http://ir.unimas.my/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf Teh, Chin Ying and Tay, Kai Meng and Lim, Cheepeng (2017) Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules. In: Emerging Trends in Neuro Engineering and Neural Computation. Series in BioEngineering, 1 . Springer, Singapore, pp. 255-264. ISBN 978-981-10-3955-3 https://link.springer.com/chapter/10.1007/978-981-10-3957-7_15 DOI 10.1007/978-981-10-3957-7_15
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Sarawak
building	UNIMAS Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
description	The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and normal trapezoid fuzzy sets are used as fuzzy membership functions. Besides that, a strong fuzzy partition strategy is used. Our empirical analysis shows that a set of multi-attribute monotone data may lead to non-monotone fuzzy If-Then rules. The same observation can be made, empirically, using adaptive neuro-fuzzy inference system (ANFIS), a well-known and popular FIS model with neural learning capability. This finding is important for the modeling of a monotone FIS model, because it shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course of developing data-driven FIS models.
format	Book Section
author	Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng
author_facet	Teh, Chin Ying Tay, Kai Meng Lim, Cheepeng
author_sort	Teh, Chin Ying
title	Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
title_short	Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
title_full	Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
title_fullStr	Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
title_full_unstemmed	Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules
title_sort	monotone data samples do not always generate monotone fuzzy if-then rules
publisher	Springer
publishDate	2017
url	http://ir.unimas.my/15755/ http://ir.unimas.my/15755/ http://ir.unimas.my/15755/ http://ir.unimas.my/15755/1/Monotone%20Data%20Samples%20Do%20Not%20Always%20%28abstract%29.pdf
first_indexed	2018-09-06T16:24:31Z
last_indexed	2018-09-06T16:24:31Z
_version_	1610876003771482112

Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules

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