A survey of fuzzy min max neural networks for pattern classification: variants and applications

Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many real-world applications. Artificial neural networks and fuzzy logic are the two most widely used models in pattern classifica...

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Main Authors:	Al Sayaydeh, Osama Nayel, Mohammed, Mohammed Falah, Lim, Chee Peng
Format:	Article
Language:	English
Published:	IEEE 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/1/A%20survey%20of%20fuzzy%20min%20max%20neural%20networks%20for%20pattern%20classification.pdf

id	ump-22646
recordtype	eprints
spelling	ump-226462019-01-28T06:26:00Z http://umpir.ump.edu.my/id/eprint/22646/ A survey of fuzzy min max neural networks for pattern classification: variants and applications Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah Lim, Chee Peng QA76 Computer software Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many real-world applications. Artificial neural networks and fuzzy logic are the two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the existing hybrid models, the family of Fuzzy Min-Max (FMM) neural networks offers a premier model for undertaking pattern classification problems. While the original FMM model is useful in terms of its capability of online learning, it suffers from several limitations in its learning procedure. Therefore, researchers have proposed numerous improvements to overcome the limitations over the years. In this paper, we conduct a comprehensive survey on the developments of FMM-based models for pattern classification. To allow researchers in selecting the most suitable FMM variants and to provide a proper guideline for future developments, this study divides the FMM variants into two main categories, namely FMM variants with and without contraction. This division facilitates understanding of the improvements on the original FMM model, as well as enables identification of the limitations that still exist in various FMM-based models. We also summarize the use of FMM and its variants in solving different benchmark and real-world pattern classification problems. In addition, future trends and research directions of FMM-based models are highlighted. IEEE 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22646/1/A%20survey%20of%20fuzzy%20min%20max%20neural%20networks%20for%20pattern%20classification.pdf Al Sayaydeh, Osama Nayel and Mohammed, Mohammed Falah and Lim, Chee Peng (2018) A survey of fuzzy min max neural networks for pattern classification: variants and applications. IEEE Transactions On Fuzzy Systems. pp. 1-11. ISSN 1063-6706 (In Press) https://doi.org/10.1109/TFUZZ.2018.2865950 https://doi.org/10.1109/TFUZZ.2018.2865950
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah Lim, Chee Peng A survey of fuzzy min max neural networks for pattern classification: variants and applications
description	Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many real-world applications. Artificial neural networks and fuzzy logic are the two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the existing hybrid models, the family of Fuzzy Min-Max (FMM) neural networks offers a premier model for undertaking pattern classification problems. While the original FMM model is useful in terms of its capability of online learning, it suffers from several limitations in its learning procedure. Therefore, researchers have proposed numerous improvements to overcome the limitations over the years. In this paper, we conduct a comprehensive survey on the developments of FMM-based models for pattern classification. To allow researchers in selecting the most suitable FMM variants and to provide a proper guideline for future developments, this study divides the FMM variants into two main categories, namely FMM variants with and without contraction. This division facilitates understanding of the improvements on the original FMM model, as well as enables identification of the limitations that still exist in various FMM-based models. We also summarize the use of FMM and its variants in solving different benchmark and real-world pattern classification problems. In addition, future trends and research directions of FMM-based models are highlighted.
format	Article
author	Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah Lim, Chee Peng
author_facet	Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah Lim, Chee Peng
author_sort	Al Sayaydeh, Osama Nayel
title	A survey of fuzzy min max neural networks for pattern classification: variants and applications
title_short	A survey of fuzzy min max neural networks for pattern classification: variants and applications
title_full	A survey of fuzzy min max neural networks for pattern classification: variants and applications
title_fullStr	A survey of fuzzy min max neural networks for pattern classification: variants and applications
title_full_unstemmed	A survey of fuzzy min max neural networks for pattern classification: variants and applications
title_sort	survey of fuzzy min max neural networks for pattern classification: variants and applications
publisher	IEEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/ http://umpir.ump.edu.my/id/eprint/22646/1/A%20survey%20of%20fuzzy%20min%20max%20neural%20networks%20for%20pattern%20classification.pdf
first_indexed	2023-09-18T22:33:50Z
last_indexed	2023-09-18T22:33:50Z
_version_	1777416463669264384

A survey of fuzzy min max neural networks for pattern classification: variants and applications

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