System Neural Network: Evolution and Change Based Structure Learning

Abstract

System evolution analytics with artificial neural networks is a challenging and path-breaking direction, which could ease intelligent processes for systems that evolve over time. In this article, we contribute an approach to do Evolution and Change Learning (ECL), which uses an evolution representor and forms a System Neural Network (SysNN). We proposed an algorithm System Structure Learning , which is divided in two steps. First step uses the evolution representor as an Evolving Design Structure Matrix (EDSM) for intelligent design learning. Second step uses a Deep Evolution Learner that learns from evolution and changes patterns of an EDSM to generate Deep SysNN. The result demonstrates application of the proposed approach to analyze four real-world system domains: software, natural-language, retail market, and movie genre. We achieved significant learning over highly imbalanced datasets. The learning from previous states formed SysNN as a feed-forward neural network, and then memorized information as an output matrix to recommend entity-connections.