Title :
ATM congestion control using a fuzzy neural network
Author :
Kwok, Alice ; McLeod, Robert
Author_Institution :
TRLabs-Winnipeg, Man., Canada
Abstract :
This paper presents a new mechanism to control the link-by-link traffic of an asynchronous transfer mode (ATM) switch. This method makes use of the linguistic ability of fuzzy set theory and logic to handle the complexity. A fuzzy neural network (FNN) will learn to control the injection rate of the previous ATM switch by issuing a signal. The FNN will learn to follow the inference method, and decide what kind of signal should be sent based on a set of rules as in the inference method
Keywords :
asynchronous transfer mode; fuzzy logic; fuzzy neural nets; fuzzy set theory; inference mechanisms; learning (artificial intelligence); telecommunication congestion control; telecommunication traffic; ATM congestion control; ATM switch; asynchronous transfer mode; complexity; fuzzy logic; fuzzy neural network; fuzzy set theory; inference method; injection rate control; linguistic ability; link-by-link traffic control; Asynchronous transfer mode; Communication system control; Communication system traffic control; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Neurons; Switches; Traffic control;
Conference_Titel :
Electrical and Computer Engineering, 1996. Canadian Conference on
Conference_Location :
Calgary, Alta.
Print_ISBN :
0-7803-3143-5
DOI :
10.1109/CCECE.1996.548277
