Cooling Growing Grid: an incremental self-organizing neural network for data exploration

Fundamental self-organizing artificial neural networks, both static (with predefined number of neurons) and incremental, are presented and goals of competitive learning are enumerated. A novel incremental self-organizing ANN Cooling Growing Grid (CGG) is proposed, which combines the advantages of static and incremental approaches and overcomes their main drawbacks. The estimation of growth direction is made less haphazard and the weights adaptation rule is modified in order to achieve better performance for highly nonuniform real-world data. The main performance measures for evaluation of reference vectors distribution are utilized in order to compare CGG with existing models.