GenSoFNN-Yager: A novel brain-inspired generic self-organizing neuro-fuzzy system realizing Yager inference

  • Authors:
  • R. J. Oentaryo;M. Pasquier;C. Quek

  • Affiliations:
  • Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

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Abstract

Pattern recognition is increasingly becoming a key component of decision support systems (DSSs) in many application areas, especially when automatically extracting semantic rules from data is a chief concern. Accordingly, this paper presents a novel evolving neuro-fuzzy DSS, the generic self-organizing fuzzy neural network realizing Yager inference (GenSoFNN-Yager), that emulates the sequential learning paradigm of the hippocampus in the brain to synthesize from low-level numerical data to high-level declarative fuzzy rules. The proposed system exhibits simple and conceptually firm computational steps that correspond closely to a plausible human logical reasoning and decision-making. Experimental results on sample benchmark problems and realistic medical diagnosis applications show the potential of the proposed system as a competent DSS.