Derivation of fuzzy membership functions using one-dimensional self-organizing maps

This paper discusses a system of self-organizing maps that approximate the fuzzy membership function for an arbitrary number of fuzzy classes. This is done through the ordering and clustering properties of one-dimensional self-organizing maps and iterative approximation of conditional probabilities of nodes in one map being the winner given that a node in the other map is the winner. Application of this system reduces fuzzy membership design time to that required to train the system of self-organizing maps.