Virtual network topology control with Oja and APEX learning

Virtual Network Topology (VNT) is any possible topology established between a subset of nodes in optical wavelength-division multiplexing (WDM) networks. This virtual topology can be designed according to the traffic load of the network. VNT control searches for a suitable virtual topology satisfying the specified requirements. Previously, VNT control based on attractor selection using Hebbian learning has been studied. This work extends the previous work with the inclusion of two new learning algorithms (i.e. Oja and APEX learning) and orthogonal projection with the aim of increasing the stability of attractors. Our results show that, both methods achieves better performance than Hebbian learning.