Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

ATM call admission control using sparse distributed memory

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
$31 $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

1 Author(s)
Hee-Yong Kwon ; Dept. of Comput. Sci., Anyang Univ., South Korea

We have proposed a neural network call admission control (CAC) method based on sparse distributed memory (SDM). CAC is a key technology of ATM network traffic control. It should be adaptable to the rapid and various changes of the ATM network environment. Conventional approaches to the ATM CAC require network analysis in detail in all cases. The optimal implementation is said to be very difficult. Therefore, neural approaches have recently been employed. However, it does not meet the adaptability requirements. We, thus, have proposed a method which is based on SDM as the neural network controller. Since SDM is a RAM-like associative memory, it has the property of good adaptability. It provides CAC with good adaptability to manage changes. Experimental results are as good as those of the previous neural approaches without additional analytical data, and without relearning from initial state

Published in:

Neural Networks,1997., International Conference on  (Volume:2 )

Date of Conference:

9-12 Jun 1997