Abstract
Abstract Neural network information processing forms the basis of a novel Intelligent Communication Network Routing(ICNR) system designed to implement optimal routing in a data communication network. The main function of the ICNR system is similar to current optimization routing algorithms with two additional innovative features. First, the relevant communications network routing “state” information necessary for determining optimal routing paths, is acquired by using a neural network with unsupervised learning capability. This neural network operates on a history of incomplete state information by performing clustering of states to estimate the complete network state vector. Secondly, the routing decisions made are learned by a neural network with a supervised learning rule which adapts to acquire the mapping between the incomplete network state vector and the optimal routing assignment and generalizes the mapping to provide sub-optimal solutions for new cases. This paper demonstrates the feasibility of using a neural network with unsupervised learning capability to estimate the communication network traffic congestion conditions necessary to implement effective routing algorithms.
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