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On a semantics for neural networks based on fuzzy quantifiers

We describe the aggregation process of the typical artificial neuron. We introduce the concept of a fuzzy linguistic quantifier and describe the process for determining the truth of propositions containing linguistic quantifiers. We show how this truth value can be viewed as the firing level of an artificial neuron. We show the relationship between fuzzy sets and neural inputs. A new class of neurons called owa‐neurons is described. A learning algorithm for this class of neurons is presented. We provide a methodology for processing information in non‐numeric neural networks. © 1992 John Wiley & Sons, Inc.