Neuro-fuzzy control in ATM networks

Douligeris, Christos; Develekos, G.

doi:10.1109/35.592110

Cited by 41 publications

(14 citation statements)

References 41 publications

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“…Further reviews on NN applications in ATM trac management problems can be found in the work of Douligeris [7], Park and Lee [25] and Tarraf et al [32].…”

Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

“…The use of neural networks (NNs) in trac management [4,7,23,25,32,39] has been gaining wide acceptance lately due to the following advantages of NNs: · ability to adapt to changes in trac conditions via on-line training, · precise models of the network processes are not required, · ability to give reasonable outputs for input patterns previously not observed, · high computational rates due to parallel hardware implementation, and · ability to perform prediction by learning the relationship between current and future values.…”

Section: Introductionmentioning

confidence: 99%

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Modular neural networks for multi-service connection admission control

Soh

Tham

2001

Computer Networks

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“…Further reviews on NN applications in ATM trac management problems can be found in the work of Douligeris [7], Park and Lee [25] and Tarraf et al [32].…”

Section: Nn-based Connection Admission Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modular neural networks for multi-service connection admission control

Soh

Tham

2001

Computer Networks

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“…Recently, use of Artificial Neural Networks (ANN) in the traffic management of ATM networks has gained momentum [4][5][6][7]15]. An adaptive controller using neural networks for congestion control in ATM multiplexers has been developed [9, l4].…”

Section: Introductionmentioning

confidence: 99%

ATM congestion control using Minimal Resource Allocation Networks (MRAN)

Soon

Sundararajan

Saratchandran

2003

Neural Computing & Applications

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This paper presents a congestion control scheme for ATM traffic using a minimal radial basis function neural network referred to as Minimal Resource Allocation Network (MRAN). Earlier studies have shown that MRAN is well suited for online adaptive control of nonlinear time varying systems as it can adjust its size by adding and pruning the hidden neurons based on the input data. Since ATM traffic is nonlinear and time varying performance of MRAN as a congestion controller is investigated here. These studies are carried out using OPNET to model the ATM traffic. The ATM traffic model consists of bursty, Variable BitRate (VBR) and custom traffic in a multiplexed form so as to generate a heavily congested traffic situation. For this scenario, the controller has to minimize the congestion episodes and maintain the Quality of Service (QoS) requirements. This paper compares the performance of the MRAN congestion controller with that of a modified Explicit Rate Indication with Congestion Avoidance (ERICA) scheme and a Back-Propagation (BP) neural controller. Simulation results indicate that MRAN controller performs better than the modified ERICA and BP controller in reducing the congestion episodes and maintaining the desirable QoS.

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“…Recently, neural networks have been used widely to deal with traffic-control-related problems in ATM networks [4][5][6][7][8][9][10][11]. Hiramatsu [4] proposed a call connection admission controller based on neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…Asynchronous Transfer Mode (ATM) is a key technology for the broadband integrated services digital network (B-ISDN), which is designed to efficiently support high speed digital voice, video, image and data services [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ATM is a connection-oriented packet switching technology.…”

Section: Introductionmentioning

confidence: 99%

Neural network training using genetic algorithms in ATM traffic control

Bourbakis²

Proceedings. IEEE International Joint Symposia on Intelligence and Systems (Cat. No.98EX174)

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There are various traditional mathematical approaches used in ATM traffic control to maintain the QoS. However, most of these approaches are not suitable for handling the wide variety of ATM services and diversity of their combinations. The precise characteristics of the source traffic are not known and the service quality requirements change over time. Building an efficient network controller which can control the network traffic is a difficult task. The advantage of using NNs in ATM is that the QoS can be accurately estimated without detailed user action models or knowledge about the switching system architecture. The disadvantage is that it will take longer time to train with ATM network changes. In this paper, we use Genetic Algorithms in Neural Network weights training for ATM Call Admission Control (CAC) and Usage Parameter Control (UPC). The simulation results have shown not only a guarantee for the QoS of all the services, but also a saving of the system bandwidth and an improvement of the throughput.

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Neuro-fuzzy control in ATM networks

Cited by 41 publications

References 41 publications

Modular neural networks for multi-service connection admission control

Modular neural networks for multi-service connection admission control

ATM congestion control using Minimal Resource Allocation Networks (MRAN)

Neural network training using genetic algorithms in ATM traffic control

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