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A Neural Network Implementation for the Traffic Control Problem on Crossbar Switch Networks

In this paper, we propose a neural network for the traffic control problem on crossbar switch networks. First, we represent this problem by an energy function, then apply the proposed neural network to update the state of the energy function until a stable state is reached. Within O(n) iteration steps, where n is the size of an n×n network, the energy function reaches a stable state which corresponds to a feasible solution of the traffic control problem. Also, the simulation results show that our neural network generates either optimal or near optimal solutions. Based on our neural network approach, many problems of applying neural networks to optimization problems are overcome, for example, the unpredictable converging time to reach a stable state, the probability of converging to a local minimum which corresponds to an invalid solution and the selecting of proper parameters of an energy function for obtaining a good (near optimal) solution, etc.