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A QoS-provisioning neural fuzzy connection admission controller for multimedia high-speed networks

机译:用于多媒体高速网络的QoS提供神经模糊连接准入控制器

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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.
机译:本文提出了一种用于多媒体高速网络中具有QoS保证的连接准入控制(CAC)的神经模糊方法。模糊逻辑系统已成功应用于处理与交通控制相关的问题,并为处理现实世界中的不精确性提供了强大的数学框架。但是,没有清晰,通用的技术可以将交通控制领域的知识映射到模糊逻辑系统的参数上。神经网络具有学习和自适应功能,可用于构建用于交通控制的智能计算算法。但是,传统方法中包含的知识很难整合到神经网络的设计中。所提出的神经模糊连接准入控制(NFCAC)方案是一种综合方法,将模糊逻辑控制器的语言控制能力与神经网络的学习能力结合在一起。这是一种智能的实现,因此它可以提供一个强大的框架来模仿现有交通控制技术中体现的专家知识,并可以构建用于交通控制的高效计算算法。我们会正确选择输入变量并设计NFCAC控制器的规则结构,以便即使在动态环境下也可以具有可靠的操作。仿真结果表明,与传统的基于有效带宽的CAC,基于模糊逻辑的CAC和基于神经网络的CAC相比,所提出的NFCAC可以实现较高的系统利用率,较高的学习速度和简单的设计流程,同时保持QoS合同。

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