@inproceedings{10.1007/978-3-540-85920-8_53,
    abstract = "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.",
    year = "2008",
    title = "Self-organizing neuro-fuzzy inference system",
    volume = "5197 LNCS",
    pages = "429-436",
    doi = "10.1007/978-3-540-85920-8\_53",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
}

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