Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/4905
Title: Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management
Authors: Mohit Aiyar
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
Issue Date: 2005
Source: Mohit, A. (2005). Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: This thesis presents a study on the use of recently developed neural networks MRAN (Minimal Resource Allocation Network) and GAP (Growing and Pruning neural network) for the performance enhancement of Call Admission Control in Asynchronous Transfer Mode (ATM) networks. GAP and MRAN generate a minimal radial basis function neural network by adding and pruning hidden neurons based on input data and are ideal for online adaptive control of fast time-varying non-linear systems. The use of GAP and MRAN in the study of call admission control schemes is new. The fast learning and accurate predictions obtained with the neural networks are shown to make better call admission control decisions under heavy traffic situations compared to conventional schemes.
URI: https://hdl.handle.net/10356/4905
DOI: 10.32657/10356/4905
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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