Fuzzy Inputs and Missing Data in Similarity - BasedHeterogeneous Neural

  • Julio J. Vald
  • Published 1999

Abstract

Fuzzy heterogeneous networks are recently introduced neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, also admitting missing data. These networks have net input functions based on similarity relations between the inputs and the weights of… (More)

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