Abstract
The paper investigates the application of neural networks to adaptive congestion control in broadband ATM networks. A neural control scheme is proposed which is a direct application of the backpropagation neural networks with those modifications required to pose the problem in the framework of a general quality-of-service control. The learning algorithms regulating traffic loads to meet performance requirements are described and validated. To illustrate the present scheme's ability to control, three examples of networks consisting of simple dynamic queuing models are studied through simulations.
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