Intelligent traffic control for ATM broadband networks

Performance results prove that a neural networks approach achieves better results, simpler and faster, than algorithmic approaches. The focus of this paper is to shed light on how neural networks (NNs) can be used to solve many of the serious problems encountered in the development of a coherent traffic control strategy in ATM networks. The main philosophy that favors neural networks over conventional programming approaches is their learning and adaptive capabilities, which can be utilized to construct adaptive (and computationally intelligent) algorithms for allocation of resources (e.g., bandwidth, buffers), thus providing highly effective tools for congestion control.<
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