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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Allende-Cid, H., Veloz, A., Salas, R., Chabert, S., & Allende, H. (2008). Self-organizing neuro-fuzzy inference system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5197 LNCS, pp. 429–436). https://doi.org/10.1007/978-3-540-85920-8_53
Mendeley helps you to discover research relevant for your work.