標題: A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks
作者: Cheng, RG
Chang, CJ
Lin, LF
電信工程研究所
電信研究中心
Institute of Communications Engineering
Center for Telecommunications Research
公開日期: 1-二月-1999
摘要: This paper proposes a neural fuzzy approach for connection admission control (CAC) with QoS guarantee in multimedia high-speed networks. Fuzzy logic systems have been successfully applied to deal with traffic-control-related problems and have provided a robust mathematical framework for dealing with real-world imprecision. However, there is no clear and general technique to map domain knowledge on traffic control onto the parameters of a fuzzy logic system. Neural networks have learning and adaptive capabilities that can be used to construct intelligent computational algorithms for traffic control. However, the knowledge embodied in conventional methods is difficult to incorporate into the design of neural networks. The proposed neural fuzzy connection admission control (NFCAC) scheme is an integrated method that combines the linguistic control capabilities of a fuzzy logic controller and the learning abilities of a neural network. It is an intelligent implementation so that it can provide a robust framework to mimic experts' knowledge embodied in existing traffic control techniques and can construct efficient computational algorithms for traffic control. We properly choose input variables and design the rule structure for the NFCAC controller so that it can have robust operation even under dynamic environments. Simulation results show that compared with a conventional effective-bandwidth-based CAC, a fuzzy-logic-based CAC, and a neural-net-based CAC, the proposed NFCAC can achieve superior system utilization, high learning speed, and simple design procedure, while keeping the QoS contract.
URI: http://dx.doi.org/10.1109/90.759332
http://hdl.handle.net/11536/31526
ISSN: 1063-6692
DOI: 10.1109/90.759332
期刊: IEEE-ACM TRANSACTIONS ON NETWORKING
Volume: 7
Issue: 1
起始頁: 111
結束頁: 121
顯示於類別:期刊論文


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  1. 000079621900010.pdf