Construction Method of Fuzzy Inference by Rule Creation

Michiharu MAEDA; Hiromi MIYAJIMA

Construction Method of Fuzzy Inference by Rule Creation

Michiharu MAEDA, Hiromi MIYAJIMA

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This paper describes two methods to construct fuzzy inference rules by the simplified fuzzy reasoning. The present methods have a construction mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create a rule unit near the selected rule which has the nearest position from the central input space for the central value. The second is to create a rule unit near the selected rule which has the minimum width for the width. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.6 pp.1509-1512

Publication Date: 2003/06/01

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Type of Manuscript: Special Section LETTER (Special Section on Papers Selected from 2002 International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC 2002))

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Michiharu MAEDA, Hiromi MIYAJIMA, "Construction Method of Fuzzy Inference by Rule Creation" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 6, pp. 1509-1512, June 2003, doi: .
Abstract: This paper describes two methods to construct fuzzy inference rules by the simplified fuzzy reasoning. The present methods have a construction mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create a rule unit near the selected rule which has the nearest position from the central input space for the central value. The second is to create a rule unit near the selected rule which has the minimum width for the width. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_6_1509/_p

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@ARTICLE{e86-a_6_1509,
author={Michiharu MAEDA, Hiromi MIYAJIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Construction Method of Fuzzy Inference by Rule Creation},
year={2003},
volume={E86-A},
number={6},
pages={1509-1512},
abstract={This paper describes two methods to construct fuzzy inference rules by the simplified fuzzy reasoning. The present methods have a construction mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create a rule unit near the selected rule which has the nearest position from the central input space for the central value. The second is to create a rule unit near the selected rule which has the minimum width for the width. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.},
keywords={},
doi={},
ISSN={},
month={June},}

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TY - JOUR
TI - Construction Method of Fuzzy Inference by Rule Creation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1509
EP - 1512
AU - Michiharu MAEDA
AU - Hiromi MIYAJIMA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2003
AB - This paper describes two methods to construct fuzzy inference rules by the simplified fuzzy reasoning. The present methods have a construction mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create a rule unit near the selected rule which has the nearest position from the central input space for the central value. The second is to create a rule unit near the selected rule which has the minimum width for the width. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.
ER -