A neural network solution for call routing with preferential call placement

A neural network solution to the problem of routing calls through a three-stage interconnection network is presented. The solution uses a Hopfield network with a binary threshold, rather than a sigmoidal function. An important feature of this solution is that the weights of the neural network are fixed for all time and are independent of the current state of the interconnection network. It is possible to implement various preferential call placement strategies through selection of external inputs to the neural network, again independent of the weights. The performance of the call placement strategy is guaranteed. The operation of the neural network solution is discussed in terms of a digital logic implementation that could be used to realize the same functionality.<<ETX>>

[1]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[2]  T.X. Brown,et al.  Neural networks for switching , 1989, IEEE Communications Magazine.

[3]  H.E. Rauch,et al.  Neural networks for routing communication traffic , 1988, IEEE Control Systems Magazine.

[4]  C. E. Rohrs,et al.  A neural network solutions for routing in three stage interconnection networks , 1990, IEEE International Symposium on Circuits and Systems.

[5]  P. Hartman Ordinary Differential Equations , 1965 .

[6]  S.C.A. Thomopoulos,et al.  Neural network implementation of the shortest path algorithm for traffic routing in communication networks , 1989, International 1989 Joint Conference on Neural Networks.

[7]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.