Fuzzy min-max neural networks

The author describes how the fuzzy min-max classifier is implemented as a neural network, explains how min- Max classes are produced, and provides two examples of operation.

Abstract

A supervised neural network classifier using a combination of min-max hyperboxes and fuzzy logic is described. A min-max hyperbox and its membership function define a fuzzy set. Each class in the neural network is a collection of labeled hyperboxes (fuzzy sets). The degree to which an input pattern belongs to a class is determined by the membership function of the winning hyperbox. Using multiple hyperbox fuzzy sets to form classes allows arbitrary numbers and shapes of classes and their respective class boundaries. The min-max classification learning procedure requires only a single pass through the data and allows online learning. The author describes how the fuzzy min-max classifier is implemented as a neural network, explains how min-max classes are produced, and provides two examples of operation.>