Fuzzy Modeling in Some Reduction Methods of Inference Rules
Summary :
This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.
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- IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.3 pp.820-828
- Publication Date
- 2001/03/01
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Nonlinear Problems
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Michiharu MAEDA, Hiromi MIYAJIMA, "Fuzzy Modeling in Some Reduction Methods of Inference Rules" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 3, pp. 820-828, March 2001, doi: .
Abstract: This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_3_820/_p
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@ARTICLE{e84-a_3_820,
author={Michiharu MAEDA, Hiromi MIYAJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fuzzy Modeling in Some Reduction Methods of Inference Rules},
year={2001},
volume={E84-A},
number={3},
pages={820-828},
abstract={This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Fuzzy Modeling in Some Reduction Methods of Inference Rules
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 820
EP - 828
AU - Michiharu MAEDA
AU - Hiromi MIYAJIMA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2001
AB - This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.
ER -
English
