Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference

Hiromi MIYAJIMA; Kazuya KISHIDA; Shinya FUKUMOTO

Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference

Hiromi MIYAJIMA, Kazuya KISHIDA, Shinya FUKUMOTO

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In order to provide a fuzzy system with learning function, numerous studies are being carried out to combine fuzzy systems and neural networks. The self-tuning methods using the descent method have been proposed. The constructive and the destructive methods are more powerful than other methods using neural networks (or descent method). On the other hand the destructive method is superior in the number of rules and inference error and inferior in learning speed to the constructive method. In this paper, we propose a new learning method combining the constructive and the destructive methods. The method is superior in the number of rules, inference error and learning speed to the destructive method. However, it is inferior in learning speed to the constructive method. Therefore, in order to improve learning speed of the proposed method, simplified learning methods are proposed. Some numerical examples are given to show the validity of the proposed methods.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.10 pp.1331-1338

Publication Date: 1995/10/25

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Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)

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Hiromi MIYAJIMA, Kazuya KISHIDA, Shinya FUKUMOTO, "Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 10, pp. 1331-1338, October 1995, doi: .
Abstract: In order to provide a fuzzy system with learning function, numerous studies are being carried out to combine fuzzy systems and neural networks. The self-tuning methods using the descent method have been proposed. The constructive and the destructive methods are more powerful than other methods using neural networks (or descent method). On the other hand the destructive method is superior in the number of rules and inference error and inferior in learning speed to the constructive method. In this paper, we propose a new learning method combining the constructive and the destructive methods. The method is superior in the number of rules, inference error and learning speed to the destructive method. However, it is inferior in learning speed to the constructive method. Therefore, in order to improve learning speed of the proposed method, simplified learning methods are proposed. Some numerical examples are given to show the validity of the proposed methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_10_1331/_p

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@ARTICLE{e78-a_10_1331,
author={Hiromi MIYAJIMA, Kazuya KISHIDA, Shinya FUKUMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference},
year={1995},
volume={E78-A},
number={10},
pages={1331-1338},
abstract={In order to provide a fuzzy system with learning function, numerous studies are being carried out to combine fuzzy systems and neural networks. The self-tuning methods using the descent method have been proposed. The constructive and the destructive methods are more powerful than other methods using neural networks (or descent method). On the other hand the destructive method is superior in the number of rules and inference error and inferior in learning speed to the constructive method. In this paper, we propose a new learning method combining the constructive and the destructive methods. The method is superior in the number of rules, inference error and learning speed to the destructive method. However, it is inferior in learning speed to the constructive method. Therefore, in order to improve learning speed of the proposed method, simplified learning methods are proposed. Some numerical examples are given to show the validity of the proposed methods.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1331
EP - 1338
AU - Hiromi MIYAJIMA
AU - Kazuya KISHIDA
AU - Shinya FUKUMOTO
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1995
AB - In order to provide a fuzzy system with learning function, numerous studies are being carried out to combine fuzzy systems and neural networks. The self-tuning methods using the descent method have been proposed. The constructive and the destructive methods are more powerful than other methods using neural networks (or descent method). On the other hand the destructive method is superior in the number of rules and inference error and inferior in learning speed to the constructive method. In this paper, we propose a new learning method combining the constructive and the destructive methods. The method is superior in the number of rules, inference error and learning speed to the destructive method. However, it is inferior in learning speed to the constructive method. Therefore, in order to improve learning speed of the proposed method, simplified learning methods are proposed. Some numerical examples are given to show the validity of the proposed methods.
ER -