Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management

Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management

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 minima...

全面介紹

Saved in:
書目詳細資料
主要作者: Mohit Aiyar
其他作者: Narasimhan Sundararajan
格式: Theses and Dissertations
出版: 2008
主題:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
在線閱讀:https://hdl.handle.net/10356/4905
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
實物特徵
總結: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.

相似書籍