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Constructive, Destructive and Simplified Learning Methods of Fuzzy Inference
Hiromi MIYAJIMA Kazuya KISHIDA Shinya FUKUMOTO
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1995/10/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)
constructive method, destructive method, fuzzy inference, simplified learning method, self-tuning,
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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.