A New Congestion Control Algorithm Based on RBFNN

Author

Weifeng, Shan ; Baohong, Meng ; Wei, Zhu

Author_Institution

Commun. & Command Acad. of PLA, Wuhan

fYear

2008

fDate

7-9 April 2008

Firstpage

1236

Lastpage

1239

Abstract

This paper presents a new sequential learning algorithm for radial basis function neural network (RBFNN) based on local projection named local projection network (LPN). Earlier studies have shown that RBFNN is well suited for online adaptive control of nonlinear time varying systems as it adjust its size by adding and pruning the hidden neurons based on the input data. Simulation results indicate that RBF with the new algorithm controller performs better in reducing the congestion episodes and maintaining the desirable QoS.

Keywords

asynchronous transfer mode; learning systems; neural nets; neurocontrollers; quality of service; radial basis function networks; telecommunication congestion control; asynchronous transfer mode; congestion control algorithm; local projection network; quality of service; radial basis function neural network; sequential learning algorithm; Asynchronous transfer mode; Bit rate; Clustering algorithms; Communication system control; Communication system traffic control; Feeds; Hilbert space; Information technology; Neural networks; Programmable logic arrays; ATM; Congestion Control; RBFNN;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology: New Generations, 2008. ITNG 2008. Fifth International Conference on

Conference_Location

Las Vegas, NV

Print_ISBN

0-7695-3099-0

Type

conf

DOI

10.1109/ITNG.2008.21

Filename

4492675