  Understanding the convergence process of neural networks is one of the most
complex and crucial issues in the field of machine learning. Despite the close
association of notable successes in this domain with the convergence of
artificial neural networks, this concept remains predominantly theoretical. In
reality, due to the non-convex nature of the optimization problems that
artificial neural networks tackle, very few trained networks actually achieve
convergence. To expand recent research efforts on artificial-neural-network
convergence, this paper will discuss a different approach based on observations
of cohesive-convergence groups emerging during the optimization process of an
artificial neural network.
