Abstract
Fuzzy sets were introduced by Zadeh in 1965 to represent and manipulate data and information that possess nonstatistical uncertainty. Computational neural networks were first discussed by McCullough and Pitts in 1943 as a means of imitating the power of biologic systems for data and information processing. Probabilistic models for data analysis, are, of course, several hundred years old. This article discusses the basic ideas of and some synergisms between probabilistic, fuzzy, and computational neural networks models as they apply to pattern recognition. We also provide a brief discussion of the relationship of both approaches to statistical pattern recognition methodologies.
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