Neurocomputations in Relational Systems

  • Authors:
  • W. Pedrycz

  • Affiliations:
  • -

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 1991

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Abstract

Strong analogies between relational structures involving some composition operators and a certain class of neural networks are described. The problem of learning the connections of the structure is addressed, and relevant learning procedures are proposed. An optimized performance index which has a strong logical flavor is proposed. Some significant implementation details are studied. Numerical examples illustrate various schemes of learning in relational structures of different levels of complexity.