ATM Connection Admission Control using modular neural networks

被引：0

作者：

Tham, CK

Soh, WS

机构：

来源：

PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON APPLICATIONS OF NEURAL NETWORKS TO TELECOMMUNICATIONS 3 | 1997年 / 3卷

关键词：

ATM congestion and traffic control; call admission control;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Neural networks, such as multi-layer perceptron (MLP) networks which converge slowly, have been applied for traffic and congestion control in ATM networks. In this paper, we present a Connection Admission Control (CAC) scheme using modular and hierarchical neural networks for predicting the resulting cell loss rate (CLR) when calls are accepted. The fast learning and accurate predictions obtained using this architecture is shown to produce near zero CLR while maintaining a high throughput.

引用

页码：71 / 78

页数：8