Analog neural optimization for ATM resource management

This paper addresses the issue of how to solve convex programming problems by analog artificial neural networks (ANNs), with applications in asynchronous transfer mode (ATM) resource management. We first show that the essential and difficult optimization problem of dimensioning the system of virtual subnetworks in ATM networks can be modeled as a convex programming task. Here the transformation of the problem into a convex programming task is a nontrivial step. We also present and analyze an analog ANN architecture that is capable of solving such convex programming tasks with time-varying penalty multipliers. The latter property makes it possible to perform quick sensitivity analysis with respect to the constraints in order to identify the bottleneck capacities in the network or those which give the highest return if we invest in extending them.