A neural-net based fuzzy admission controller for an ATM network

Authors:
Ray-Guang Cheng;Chung-Ju Chang
Affiliations:
Department of Communication Engineering and Center for Telecommunications Research, National Chiao Tung University, Hsinchu, Taiwan;Department of Communication Engineering and Center for Telecommunications Research, National Chiao Tung University, Hsinchu, Taiwan
Venue:
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Year:
1996

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Abstract

This paper proposes a neural fuzzy connection admission control (NFCAC) scheme, which combines benefits of fuzzy logic controller and learning abilities of the neural-net, to solve the connection admission control (CAC) problems in ATM networks. Recently, fuzzy logic systems have been successfully applied to deal with the traffic control related problems and provided a robust mathematical framework for dealing with "real-world" imprecision; multi-layer neural networks are capable of producing complex decisions with arbitrarily nonlinear boundaries and they have been used as solution for the CAC. However, the application of neural network or fuzzy logic system to CAC exists some difficulties in real operation. The proposed NFCAC solves the difficulties by combining the benefits of the existing traffic control mechanicims, linguistic control strategy of the fuzzy logic controller and the learning ability of neural-net. Simulation results show that the proposed NFCAC saves a large amount of training time and simplifies the design procedure of a CAC controller but provides a superior system utilization, while keeping the QoS contract, than either neural network or fuzzy logic system does.