A VLSI architecture for fast clustering with fuzzy ART neural networks

Abstract

The hardware implementation of the fuzzy ART neural network applied to a demanding real time radar signal clustering problem is investigated. To obtain efficient solutions for implementing this neural network with dedicated hardware, the network's algorithm is reformulated, and then a novel fuzzy ART system architecture is proposed. This system architecture is composed of a global comparator and several identical elementary modules (EMs), each one emulating a number of neurons. The general architecture of each EM consists of a local comparator, dividers, neural processors, and a block of memory.