Implementation of the fuzzy ART neural network for fast clustering of radar pulses

Author

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

Author_Institution

Comput. Sci. Dept., Quebec Univ., Montreal, Que., Canada

Volume

2

fYear

1998

fDate

31 May-3 Jun 1998

Firstpage

458

Abstract

A real time radar signal clustering problem is resolved by a dedicated hardware implementation of the fuzzy ART neural network. This novel architecture implements a reformulated algorithm for high speed clustering. The proposed dedicated digital VLSI system is composed of cascadable integrated circuits, each one containing several neural processors, comparators, a divider and blocks of RAM. This efficient solution was designed and will be implemented in the near future. The basic component requires 74 K gates and occupies an area of 52.5 mm² in a 0.8 μm BiCMOS technology. Each chip process an input pattern for 32 neurons every 2 μs

Keywords

ART neural nets; BiCMOS digital integrated circuits; VLSI; comparators (circuits); fuzzy neural nets; neural chips; radar signal processing; real-time systems; 0.8 micron; 2 mus; BiCMOS technology; comparators; dedicated hardware implementation; digital VLSI system; fuzzy ART neural network; input pattern; neural processors; radar pulses; real time; signal clustering problem; BiCMOS integrated circuits; Clustering algorithms; Digital integrated circuits; Fuzzy neural networks; Neural network hardware; Neural networks; Radar; Signal resolution; Subspace constraints; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1998. ISCAS '98. Proceedings of the 1998 IEEE International Symposium on

Conference_Location

Monterey, CA

Print_ISBN

0-7803-4455-3

Type

conf

DOI

10.1109/ISCAS.1998.706975

Filename

706975