DocumentCode
285241
Title
The application of noisy reward/penalty learning to pyramidal pRAM structures
Author
Guan, Yelin ; Clarkson, Trevor G. ; Gorse, Denise ; Taylor, John G.
Author_Institution
Dept. of Electron. & Electr. Eng., King´´s Coll. London, UK
Volume
3
fYear
1992
fDate
7-11 Jun 1992
Firstpage
660
Abstract
It is shown that the addition of noise during probabilistic RAM (pRAM) training develops the property of generalization and therefore the ability to recognize patterns in noisy images. Global reward/penalty learning applied to the pRAM was shown to be an efficient training method that was also hardware-reliable. Results are presented for a pRAM net which show that successful discrimination of patterns can be achieved in the presence of over 45% noise, with a 20% confidence margin
Keywords
inference mechanisms; learning (artificial intelligence); neural nets; random-access storage; noisy reward/penalty learning; pattern recognition; pyramidal pRAM structures; training; Councils; Learning; Noise figure; Noise generators; Noise level; Phase change random access memory; Pixel; Probability; Signal to noise ratio; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.227099
Filename
227099
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