Performance-guided Neural Network for Self-Organising Network Management
Sin Wee Lee, Dominic Palmer-Brown, Jonathan A. Tepper, Chris Roadknight · UEL Research Repository (University of East London) · 2002
A neural network architecture is introduced for real-time learning of input sequences using external performance feedback. Some aspects of Adaptive Resonance Theory (ART) networks [1] are applied because they are able to function in a fast real-time adaptive active network environment where user requests and new proxylets (services) are constantly being introduced over time [2,3]. The architecture learns, self-organis es and self-stabilises in response to user requests, mapping the requests according to the types of proxylets available. However, in order make the neural networks respond to performance feedback, we introduce a modification to the original ART1 network in the form of the ‘snap-drift’ algorithm, that uses fast convergent, minimalist learning (snap) when the overall network performance is poor, and slow learning (drift towards user request input pattern) when the performance is good. Preliminary simulations evaluate the two-tiered architecture using a simple operating environment consisting of simulated training and test data.