By Topic

Analysis of quantization effects in a digital hardware implementation of a fuzzy ART neural network algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
M. -A. Cantin ; Dept. of Electr. & Comput. Eng., Ecole Polytech. de Montreal, Que., Canada ; Y. Blaguiere ; Y. Sarvaria ; P. Lavoie
more authors

A reformulated Adaptive Resonance Theory (ART) neural network algorithm has recently been implemented in digital hardware. Naturally, the fixed point, fixed word length data format used causes some output differences with respect to floating point computer simulation. These differences are observed when using realistic input data. The effects of input quantization and the accumulation of round off errors in the arithmetic operations making up the algorithm are analyzed. Even a small quantization or round off error can trigger a change in the clustering produced. This does not mean that the clustering is not valid. Indeed, the validity of the clustering can be comparable to that obtained by floating point computer simulation, provided the word length is sufficient. This is verified on realistic input data consisting of radar pulses received from a number of emitters

Published in:

Circuits and Systems, 2000. Proceedings. ISCAS 2000 Geneva. The 2000 IEEE International Symposium on  (Volume:3 )

Date of Conference: