Adaptive Neural Congestion Controller for ATM Network with Heavy Traffic
N. Hock Soon, N. Sundararajan, P. Saratchandran · 2000
This paper presents an adaptive control scheme using a newly developed Minimal Resource Allocation Network (MRAN) to solve the traffic congestion problem in ATM networks. MRAN generates a minimal radial basis function neural network by adding and pruning hidden neurons based on the input data and is ideal for on-line adaptive control for fast time varying nonlinear systems. The ATM traffic modeling is carried out using the well-known network simulation software OPNET for multiplexed traffic (combining both speech and video signals). Performance of MRAN controller is compared with conventional method and Back-Propagation (BP) neural network controller with the aim of minimizing the congestion episodes and maintaining the quality. Simulation results indicate that MRAN controller performs better than both conventional and BP controller in reducing the congestion and maintaining a better quality of the traffic.