A hierarchical structure neural network for aggregate bandwidth allocation of heterogeneous sources in ATM networks

Author

Benjapolakul, Watit ; Vakulchai, Chanamet

Author_Institution

Dept. of Electr. Eng., Chulalongkorn Univ., Bangkok, Thailand

fYear

2000

fDate

2000

Firstpage

395

Lastpage

398

Abstract

This paper proposes an application of neural network for aggregate bandwidth allocation of heterogeneous sources in ATM networks. A feedforward hierarchical neural network and backpropagation training method are used for recognizing the relation between trained input and bandwidth output calculated from exact analysis. The results show that the proposed neural network can allocate more accurate bandwidth than the approximate method-asymptotic analysis with a reasonably fast speed compared to that of the approximation method

Keywords

asynchronous transfer mode; backpropagation; bandwidth allocation; broadband networks; feedforward neural nets; telecommunication computing; ATM networks; aggregate bandwidth allocation; backpropagation training method; feedforward hierarchical neural network; heterogeneous sources; hierarchical structure neural network; Aggregates; Approximation methods; Asynchronous transfer mode; Backpropagation; Bandwidth; Channel allocation; Electronic mail; Equations; Neural networks; Quality of service;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on

Conference_Location

Tianjin

Print_ISBN

0-7803-6253-5

Type

conf

DOI

10.1109/APCCAS.2000.913518

Filename

913518