Self-organizing neuro-fuzzy inference system

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers’s ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user’s performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.