NDLI: A VLSI architecture for fast clustering with fuzzy ART neural networks

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

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

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A VLSI architecture for fast clustering with fuzzy ART neural networks

Content Provider	IEEE Xplore Digital Library
Author	Granger, E. Blaquiere, Y. Savaria, Y. Cantin, M.-A. Lavoie, P.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Ecole Polytech. de Montreal, Que., Canada (Granger, E.)
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.
Starting Page	117
Ending Page	125
File Size	603362
Page Count	9
File Format	PDF
ISBN	0818674563
DOI	10.1109/NICRSP.1996.542752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-08-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural network hardware Pulse measurements Subspace constraints Neural networks Neurons Clustering algorithms Very large scale integration Fuzzy neural networks Radar countermeasures Fuzzy systems
Content Type	Text
Resource Type	Article

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