自己組織化を利用した一般化ファジー推論ニューラルネットワーク

抄録

A new model of Generalized Fuzzy Inference Neural Network (GFINN) is proposed in this paper. The network consists of three layers: the input-output layer, the if layer, and the then layer. In each layer, there are the operational nodes. The GFINN can perform three representative fuzzy inference methods by changing the connectivity and the operational nodes. There are three learning precesses in the GFINN: the self-organizing process, the rule-integration precess, and the LMS learning process. Especially in the rule-integration process, the GFINN employs two feature maps in order to integrate appropriate rules effectively. Computer simulations are carried out to show the superiority of the GFINN over the back-propagation networks.