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A Monotonicity Index for the Monotone Fuzzy Modeling Problem

In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathema...

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Main Authors: Kai, M.T, Chee, P.L, Chin, Y.T, See , H.L
Format: Conference or Workshop Item
Language:English
Published: IEEE 2012
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://ir.unimas.my/id/eprint/2729/1/login.jsp_reason%3DnotIncluded%26url%3Dhttp_%252F%252Fieeexplore.ieee.org%252FXplore%252Ferror.jsp
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spelling my.unimas.ir.27292015-03-24T00:48:49Z http://ir.unimas.my/id/eprint/2729/ A Monotonicity Index for the Monotone Fuzzy Modeling Problem Kai, M.T Chee, P.L Chin, Y.T See , H.L QA Mathematics QA75 Electronic computers. Computer science In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method. IEEE 2012 Conference or Workshop Item NonPeerReviewed text en http://ir.unimas.my/id/eprint/2729/1/login.jsp_reason%3DnotIncluded%26url%3Dhttp_%252F%252Fieeexplore.ieee.org%252FXplore%252Ferror.jsp Kai, M.T and Chee, P.L and Chin, Y.T and See , H.L (2012) A Monotonicity Index for the Monotone Fuzzy Modeling Problem. In: WCCI 2012 IEEE World Congress on Computational Intelligence June, 10-15, 2012 , Brisbane, Australia, pp. 456-463..
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 QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Kai, M.T
Chee, P.L
Chin, Y.T
See , H.L
A Monotonicity Index for the Monotone Fuzzy Modeling Problem
description In this paper, the problem of maintaining the (global) monotonicity and local monotonicity properties between the input(s) and the output of an FIS model is addressed. This is known as the monotone fuzzy modeling problem. In our previous work, this problem has been tackled by developing some mathematical conditions for an FIS model to observe the monotonicity property. These mathematical conditions are used as a set of governing equations for undertaking FIS modeling problems, and have been extended to some advanced FIS modeling techniques. Here, we examine an alternative to the monotone fuzzy modeling problem by introducing a monotonicity index. The monotonicity index is employed as an approximate indicator to measure the fulfillment of an FIS model to the monotonicity property. It allows the FIS model to be constructed using an optimization method, or be tuned to achieve a better performance, without knowing the exact mathematical conditions of the FIS model to satisfy the monotonicity property. Besides, the monotonicity index can be extended to FIS modeling that involves the local monotonicity problem. We also analyze the relationship between the FIS model and its monotonicity property fulfillment, as well as derived mathematical conditions, using the Monte Carlo method.
format Conference or Workshop Item
author Kai, M.T
Chee, P.L
Chin, Y.T
See , H.L
author_facet Kai, M.T
Chee, P.L
Chin, Y.T
See , H.L
author_sort Kai, M.T
title A Monotonicity Index for the Monotone Fuzzy Modeling Problem
title_short A Monotonicity Index for the Monotone Fuzzy Modeling Problem
title_full A Monotonicity Index for the Monotone Fuzzy Modeling Problem
title_fullStr A Monotonicity Index for the Monotone Fuzzy Modeling Problem
title_full_unstemmed A Monotonicity Index for the Monotone Fuzzy Modeling Problem
title_sort monotonicity index for the monotone fuzzy modeling problem
publisher IEEE
publishDate 2012
url http://ir.unimas.my/id/eprint/2729/1/login.jsp_reason%3DnotIncluded%26url%3Dhttp_%252F%252Fieeexplore.ieee.org%252FXplore%252Ferror.jsp
http://ir.unimas.my/id/eprint/2729/
_version_ 1644509168401383424
score 13.211869

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