An efficient mapping of Fuzzy ART onto a neural architecture

Abstract

A novel mapping of the Fuzzy ART algorithm onto a neural network architecture is described. The architecture does not utilize bi-directional synapses, weight transport, or weight duplication, and requires one fewer layer of processing elements than the architecture originally proposed by Carpenter, Grossberg, & Rosen (1991a). In the new architecture, execution of the algorithm takes constant time per input vector regardless of the relationship between the input and existing templates, and several control signals are eliminated. This mapping facilitates hardware implementation of Fuzzy ART and furthermore serves as a tool for envisioning and understanding the algorithm. Keywords: Fuzzy ART, Fuzzy ARTMAP, parallel hardware, neural architecture. Fuzzy ART is a clustering algorithm that operates on vectors with analog-valued elements (Carpenter, Grossberg, & Rosen, 1991a). Adding a further layer of processing to Fuzzy ART yields a supervised clustering algorithm, Fuzzy ARTMAP (Carpenter, G..