Generalized fuzzy inference neural network using a self-organizing feature map

A new model for generalized fuzzy inference neural networks (GFINN) is proposed in this paper. The networks consist of three layers: an input-output layer, an if layer, and a then layer. In each layer, there are the operational nodes. A GFINN can perform three representative fuzzy inference methods by changing the connectivity and the operational nodes. There are three learning processes in a GFINN: a self-organizing process, a rule-integration process, and a LMS learning process. In the rule-integration process, a GFINN employs two feature maps in order to integrate appropriate rules effectively. Computer simulations were carried out to show the superiority of a GFINN over back-propagation networks.