Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference

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概要

• 論文の詳細を見る

• 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 [1], [2]. 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.

• 社団法人電子情報通信学会の論文
• 1995-10-25

著者

• Fukumoto Shinya

  Faculty Of Engineering Kagoshima University

• KISHIDA Kazuya

  Department of Electronic Control Engineering, Kagoshima National College of Technology

• MIYAJIMA Hiromi

  Faculty of Engineering, Kagoshima University

• Kishida Kazuya

  Faculty of Engineering, Kagoshima University

• MIYAJIMA Hiromi

  Department of Electrical and Electronics Engineering, Faculty of Engineering, Kagoshima University

• Kishida K

  Department Of Electronic Control Engineering Kagoshima National College Of Technology

• Miyajima H

  Department Of Electrical And Electronics Engineering Faculty Of Engineering Kagoshima University

• Miyajima Hiromi

  Faculty Of Engineering Kagoshima University

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