A destructive learning method of fuzzy inference rules
Shiro Fukumoto, Hiromi Miyajima, Kazuya Kishida, Y. Nagasawa · 2002
In order to construct a fuzzy system with a learning function, numerous studies combining fuzzy systems and neural networks (or descent method) are being carried out. The self-tuning method using the descent method has been proposed by Ichihashi et al. (1991) and it is known that the constructive method is more powerful than other methods using neural networks (or descent method). But this method does not have a sufficient generalization capability or an expressing capability for the acquired knowledge. In this paper, we propose a new learning method called a destructive method of fuzzy inference rules by the descent method. And we show that the destructive method is superior in the number of rules and inference errors but inferior in learning speed to the constructive one. Further more, in order to improve learning speed, we propose a learning method combining the constructive and the destructive methods. Some numerical examples are given to show the validity of the proposed methods, and applications of these methods to the obstacle avoidance problem are shown.>