An Effective Training Pattern Processing Method for ATM Connection Admission Control Using the Neural Network
نویسندگان
چکیده
منابع مشابه
ATM Connection Admission Control using Modular Neural Networks
Neural networks, such as multi-layer perceptron (MLP) networks which converge slowly, have been applied for tra c and congestion control in ATM networks. In this paper, we present a Connection Admission Control (CAC) scheme using modular and hierarchical neural networks for predicting the resulting cell loss rate (CLR) when calls are accepted. The fast learning and accurate predictions obtained...
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The congestion control problem is one of the challenging problems facing ATM designers. Proper control of congestion is absolutely necessary for providing QoS for all the services supported by the network. Multimedia sources such as voice and video are bursty in nature. There may be many bursts during a call and the bursts themselves may have a variable number of cells. Since the idea of high-s...
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The Connection Admission Control(CAC) is part of ATM traffic control and consists of a framework that optimizes network usage while ensuring the desired QoS for the services. The CAC approach we propose for ATM networks is based on a effective bandwidth allocation strategy. This paper addresses the effective bandwidth computation based on a probabilistic model for cell level QoS and call level ...
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A number of attempts have been made recently (roughly since 1990) to implement connection admission control (CAC) in ATM networks by means of neural networks. These attempts use various methods and have met with varying levels of success. They all try to solve some very serious networking problems related to the inadequacies of conventional algorithmic computing. Given that there has been littl...
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ژورنال
عنوان ژورنال: The KIPS Transactions:PartB
سال: 2002
ISSN: 1598-284X
DOI: 10.3745/kipstb.2002.9b.2.173