A robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure

Self-organizing neural network which is an unsupervised learning algorithm is to discover the inherent relationships of data. Such technique has become an important tool for data mining, machine learning and pattern recognition. Most self-organizing neural networks have a difficulty in reflecting data distributions precisely if data distributions are very complex. And meanwhile, it is also hard for these algorithms to learn new data incrementally without destroying the previous learnt data. In this paper, we propose a robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure (REISOD). It can adjust the scale of network automatically to adapt the scale of the data set and learn new data incrementally with preserving the former learnt results. Moreover, several experiments show that our algorithm can reflect data distributions precisely.