Fuzzy min-max classification with neural networks

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Abstract
A feedforward neural network classifier that uses min-max vector pairs to define classes is described. This two-layer neural network utilizes a supervised learning rule to build a set of classes. Each node in the output layer of the network represents a class. During recall each class node produces an output value that represents the degree to which the input pattern fits within the represented classes. This fuzzy neural network is ideally suited to applications that have very little data available to define classes. The author provides a brief overview of fuzzy sets and fuzzy pattern classification, a description of fuzzy min-max classification and its neural network implementation, and an example of the classification operation.

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