Network topology selection with multistate neural memories

We propose an efficient method utilizing neural memories for network topology selection. More specifically, we focus on virtual topology reconfiguration (VTR) problem in optical networks. One highly adaptive method that uses neural memories called Attractor Selection Based (ASB) algorithm was proposed before. However, ASB has an important drawback: it can work only with binary topologies since ASB uses binary neurons. In this work, our method is built on the same principles as ASB, yet it is capable of working with multistate topologies. By introducing multistate auto-associative memories, the information within a virtual network becomes finer grained than a binary state topology. The method we propose achieves a 60% performance improvement over ASB, and a 21% reduction in processing time in the simulations.