A VLSI architecture for fast clustering with fuzzy ART neural networks

Author

Granger, E. ; Blaquière, Y. ; Savaria, Y. ; Cantin, M.-A. ; Lavoie, P.

Author_Institution

Dept. of Electr. & Comput. Eng., Ecole Polytech. de Montreal, Que., Canada

fYear

1996

Firstpage

117

Lastpage

125

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

Keywords

ART neural nets; VLSI; fuzzy neural nets; neural chips; radar signal processing; VLSI architecture; dividers; fast clustering; fuzzy ART neural networks; global comparator; local comparator; neural processors; real time radar signal clustering; Clustering algorithms; Fuzzy neural networks; Fuzzy systems; Neural network hardware; Neural networks; Neurons; Pulse measurements; Radar countermeasures; Subspace constraints; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, 1996. Proceedings., International Workshop on

Conference_Location

Venice

Print_ISBN

0-8186-7456-3

Type

conf

DOI

10.1109/NICRSP.1996.542752

Filename

542752