ATM communications network control by neural network

A learning control method using neural networks for service quality control in the asynchronous transfer mode (ATM) network is described. An ATM network is a high-speed packet-switching network for the data transmission layer of B-ISDN (broadband integrated services digital network) which provides multimedia services, including voice, data and video. Service quality control is one of the most crucial issues in realizing a flexible ATM network. It is a challenging research task to build an efficient network controller that can control the network traffic even when the precise characteristics of the source traffic are not known and the service quality requirements change over time. The proposed ATM network controller is flexible in function and simple in implementation because neural networks using backpropagation learn the relations between the offered traffic and service qualities. A training data selection method called leaky pattern tables is proposed for learning the accurate relations. The performance of the proposed controller is evaluated by simulation of a basic call regulation model.<>