A new recurrent neural network adaptive approach for host-gate way rate control protocol within intranets using ATM ABR service

Abstract

In this paper, a new neural network adaptive control strategy based on Host Gate Way Rate Control Protocol (HGRCP) is proposed for intranet congestion management. The control algorithm is based on the Elman recurrent neural network via using the ABR service of an ATM backbone network. Simulations confirm that the proposed algorithm will produce lower queue level variance at the gateway. Meanwhile, the learning capability can be improved significantly.