A NEURAL NETWORK APPROACH TO THE TRAFFIC CONTROL PROBLEM IN REVERSE BASE-LINE NETWORKS

Abstract

A neural network for the traffic control problem applied to reverse baseline networks has been proposed in this paper. This problem has been first represented by an energy function. A neural network is applied for maximizing die energy of the function under the constraints of the reverse baseline network. The number of iteration steps in our neural network is limited by a performed upper bound O(n), where n is the size of an n x n network. The throughputs of our neural network have been shown by the empirical results to be better than the conventional algorithm (modified Bipartite Matching Algorithm) when the packet densities rise higher than 50%.