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...
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| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/4905 |
| _version_ | 1826127288120377344 |
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| author | Mohit Aiyar |
| author2 | Narasimhan Sundararajan |
| author_facet | Narasimhan Sundararajan Mohit Aiyar |
| author_sort | Mohit Aiyar |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:06:25Z |
| format | Thesis |
| id | ntu-10356/4905 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T07:06:25Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/49052023-07-04T17:00:02Z Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management Mohit Aiyar Narasimhan Sundararajan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies 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. MASTER OF ENGINEERING (EEE) 2008-09-17T10:01:08Z 2008-09-17T10:01:08Z 2005 2005 Thesis Mohit, A. (2005). Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/4905 10.32657/10356/4905 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies Mohit Aiyar Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title | Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title_full | Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title_fullStr | Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title_full_unstemmed | Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title_short | Growing and pruning (GAP) RBF networks for call admission control in ATM traffic management |
| title_sort | growing and pruning gap rbf networks for call admission control in atm traffic management |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies |
| url | https://hdl.handle.net/10356/4905 |
| work_keys_str_mv | AT mohitaiyar growingandpruninggaprbfnetworksforcalladmissioncontrolinatmtrafficmanagement |