Yoo Jae Taek, Kim Choon Seop, Kim Yong Woo, Kim Young Han, Lee Kwang Hyung

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 4, No. 8, pp. 2070-2079, Aug. 1997
10.3745/KIPSTE.1997.4.8.2070,   PDF Download:

Abstract

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

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