EXPERIMENTS WITH SIMPLE NEURAL NETWORKS FOR REAL-TIME CONTROL

We demonstrate the practical ability of neural networks (NN's) trained in supervised mode to extract useful control ''knowledge'' from a lar ge, high dimensional empirical database, and then to deliver almost op timal control in ''real time.'' In particular, this paper describes ex periments with NN-based controllers for allocating bandwidth capacity in a telecommunications network, This system was proposed in order to overcome a ''real time'' response constraint, Two basic architectures, each consisting of a combination of two methods, are evaluated: 1) a feedforward network-heuristic combination and 2) a feedforward network -recurrent network combination, These architectures are compared again st a Linear programming (LP) optimizer as a benchmark, This LP optimiz er was also used as a teacher to label the data samples for the feedfo rward NN training algorithm, NN-based solutions are very accurate (sim ilar to 98% of optimal throughput) and, in contrast to the algorithmic approach, can be delivered in ''real time.'' It is found that while t he ''human'' generated heuristics (greedy search optimization) fail to find a solution in approximately 30% of cases, the best NN fails only in 4.9% of cases, Moreover, it has been found that in spite of the ve ry high dimensionality of the problem (55 inputs and 126 outputs), the solution can be delivered by surprisingly compact NN's, with as littl e as around 1000 synaptic weights. This proves that on this occasion t he NN's were able to extract simple but powerful ''heuristics'' hidden in the complex sets of numerical data.