An incremental self-organizing neural network based on enhanced competitive Hebbian learning

Self-organizing neural networks are important tools for realizing unsupervised learning. Recently, a difficult task has involved the incremental, efficient and robust learning in noisy environments. Most of the existing techniques are poor in this regard. In this paper, we first propose a new topology generating method called enhanced competitive Hebbian learning (enhanced CHL), and then propose a novel incremental self-organizing neural network based on the enhanced CHL method, called enhanced incremental growing neural gas (Hi-GNG). The experiments presented in this paper show that the Hi-GNG algorithm can automatically and efficiently generate a topological structure with a suitable number of neurons and that the proposed algorithm is robust to noisy data.