http://iet.metastore.ingenta.com
1887

Perfect auto-associators Using RAM type nodes

Perfect auto-associators Using RAM type nodes

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A RAM-based auto-associative neural network is described which has several desirable properties, including high storage capacity and absence of minima during recall. The system is implemented as a set of generalising RAM (GRAM) type nodes. The generalisation procedure is described, and comparisons with other types of autoassociator are drawn.

References

    1. 1)
      • Kan, W., Aleksander, I.: `A probabilistic logic neuron network for associative learning', Proc. IEEE Int. Conf. on neural networks, 1988, San Diego, p. 541–548.
    2. 2)
      • Aleksander, I.: `Ideal neurons for neural computers', Proc. of Int. Conf. on Parallel Processing in Neural Systems and Computers, 1990, North Holland Elsevier Science, p. 225–232.
    3. 3)
      • R.P. Lippmann . An introduction to computing with neural nets. IEEE ASSP Magazine , 4 - 22
    4. 4)
      • L. Tarassenko , J.N. Tombs , J.H. Reynolds . Neural network architectures for content addressable memory. IEEE Proc. F , 1 , 33 - 39
http://iet.metastore.ingenta.com/content/journals/10.1049/el_19910501
Loading

Related content

content/journals/10.1049/el_19910501
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address