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On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection

Shang-Ming Zhou, J.M. Garibaldi, R.I. John, F. Chiclana, Shang-ming Zhou Orcid Logo

IEEE Transactions on Fuzzy Systems, Volume: 17, Issue: 3, Pages: 654 - 667

Swansea University Author: Shang-ming Zhou Orcid Logo

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DOI (Published version): 10.1109/TFUZZ.2008.928597

Abstract

Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of...

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Published in: IEEE Transactions on Fuzzy Systems
ISSN: 1063-6706 1941-0034
Published: IEEE TRANSACTIONS ON FUZZY SYSTEMS 2009
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URI: https://cronfa.swan.ac.uk/Record/cronfa10027
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spelling 2019-07-17T14:57:02.7039316 v2 10027 2012-03-21 On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection 118578a62021ba8ef61398da0a8750da 0000-0002-0719-9353 Shang-ming Zhou Shang-ming Zhou true false 2012-03-21 BMS Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, ω1 -values, and ω2 -values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the ω1 -values and ω2 -values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed "forward selection" and "backward elimination") are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models. Journal Article IEEE Transactions on Fuzzy Systems 17 3 654 667 IEEE TRANSACTIONS ON FUZZY SYSTEMS 1063-6706 1941-0034 11 6 2009 2009-06-11 10.1109/TFUZZ.2008.928597 COLLEGE NANME Biomedical Sciences COLLEGE CODE BMS Swansea University 2019-07-17T14:57:02.7039316 2012-03-21T16:17:09.0000000 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Shang-Ming Zhou 1 J.M. Garibaldi 2 R.I. John 3 F. Chiclana 4 Shang-ming Zhou 0000-0002-0719-9353 5 0010027-26042019162714.pdf PaperInForthcoming.pdf 2019-04-26T16:27:14.9470000 Output 486576 application/pdf Accepted Manuscript true 2019-04-26T00:00:00.0000000 true eng
title On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
spellingShingle On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
Shang-ming Zhou
title_short On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
title_full On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
title_fullStr On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
title_full_unstemmed On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
title_sort On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection
author_id_str_mv 118578a62021ba8ef61398da0a8750da
author_id_fullname_str_mv 118578a62021ba8ef61398da0a8750da_***_Shang-ming Zhou
author Shang-ming Zhou
author2 Shang-Ming Zhou
J.M. Garibaldi
R.I. John
F. Chiclana
Shang-ming Zhou
format Journal article
container_title IEEE Transactions on Fuzzy Systems
container_volume 17
container_issue 3
container_start_page 654
publishDate 2009
institution Swansea University
issn 1063-6706
1941-0034
doi_str_mv 10.1109/TFUZZ.2008.928597
publisher IEEE TRANSACTIONS ON FUZZY SYSTEMS
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, ω1 -values, and ω2 -values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the ω1 -values and ω2 -values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed "forward selection" and "backward elimination") are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models.
published_date 2009-06-11T03:10:39Z
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score 11.012678

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