Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System

To maintain the monotonicity property of a fuzzy inference system, a monotonically-ordered and complete set of fuzzy rules is necessary. However, monotonically-ordered fuzzy rules are not always available, e.g. errors in human judgements lead to non-monotone fuzzy rules. The focus of this paper is o...

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المؤلفون الرئيسيون: Tay, Kai Meng, Liew, Meng Pang, Chee, Peng Lim
التنسيق: E-Article
اللغة:English
منشور في: IEEE 2016
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الوصول للمادة أونلاين:http://ir.unimas.my/id/eprint/12132/1/monotone%20fuzzy%20rule%20relabeling%20for%20the%20zero%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/12132/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7429714
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spelling my.unimas.ir.121322017-04-12T02:28:02Z http://ir.unimas.my/id/eprint/12132/ Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System Tay, Kai Meng Liew, Meng Pang Chee, Peng Lim QA75 Electronic computers. Computer science TA Engineering (General). Civil engineering (General) To maintain the monotonicity property of a fuzzy inference system, a monotonically-ordered and complete set of fuzzy rules is necessary. However, monotonically-ordered fuzzy rules are not always available, e.g. errors in human judgements lead to non-monotone fuzzy rules. The focus of this paper is on a new monotone fuzzy rule relabeling (MFRR) method that is able to relabel a set of non-monotone fuzzy rules to meet the monotonicity property with reduced computation. Unlike the brute-force approach, which is susceptible to the combinatorial explosion problem, the proposed MFRR method explores within a reduced search space to find the solutions; therefore decreasing the computational requirements. The usefulness of the proposed method in undertaking Failure Mode and Effect Analysis problems is demonstrated using publicly available information. The results indicate that the MFRR method can produce optimal solutions with reduced computational time. IEEE 2016 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/12132/1/monotone%20fuzzy%20rule%20relabeling%20for%20the%20zero%20%28abstract%29.pdf Tay, Kai Meng and Liew, Meng Pang and Chee, Peng Lim (2016) Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System. IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1455-1463. ISSN 1063-6706 (In Press) http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7429714 10.1109/TFUZZ.2016.2540059
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA75 Electronic computers. Computer science
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
TA Engineering (General). Civil engineering (General)
Tay, Kai Meng
Liew, Meng Pang
Chee, Peng Lim
Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
description To maintain the monotonicity property of a fuzzy inference system, a monotonically-ordered and complete set of fuzzy rules is necessary. However, monotonically-ordered fuzzy rules are not always available, e.g. errors in human judgements lead to non-monotone fuzzy rules. The focus of this paper is on a new monotone fuzzy rule relabeling (MFRR) method that is able to relabel a set of non-monotone fuzzy rules to meet the monotonicity property with reduced computation. Unlike the brute-force approach, which is susceptible to the combinatorial explosion problem, the proposed MFRR method explores within a reduced search space to find the solutions; therefore decreasing the computational requirements. The usefulness of the proposed method in undertaking Failure Mode and Effect Analysis problems is demonstrated using publicly available information. The results indicate that the MFRR method can produce optimal solutions with reduced computational time.
format E-Article
author Tay, Kai Meng
Liew, Meng Pang
Chee, Peng Lim
author_facet Tay, Kai Meng
Liew, Meng Pang
Chee, Peng Lim
author_sort Tay, Kai Meng
title Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
title_short Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
title_full Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
title_fullStr Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
title_full_unstemmed Monotone Fuzzy Rule Relabeling for the Zero-Order TSK Fuzzy Inference System
title_sort monotone fuzzy rule relabeling for the zero-order tsk fuzzy inference system
publisher IEEE
publishDate 2016
url http://ir.unimas.my/id/eprint/12132/1/monotone%20fuzzy%20rule%20relabeling%20for%20the%20zero%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/12132/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7429714
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score 13.250246