The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo trata da modelagem fuzzy em alguns métodos de redução de regras de inferência com gradiente descendente. São apresentados métodos de redução, que possuem um mecanismo de redução da unidade de regra que é aplicável em três parâmetros - o valor central e a largura da função de pertinência na parte antecedente, e o número real na parte consequente - que constituem o sistema difuso padrão. Nas técnicas actuais, o número necessário de regras é definido antecipadamente e as regras são eliminadas sequencialmente até ao número pré-especificado. Estes métodos indicam que existem outras técnicas além da abordagem de redução introduzida anteriormente. Resultados experimentais são apresentados para mostrar que a eficácia difere entre as técnicas propostas de acordo com o erro médio de inferência e o número de iterações de aprendizagem.
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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 -