A comparison between the performance of feed forward neural networks and the supervised growing neural gas algorithm

The Supervised Growing Neural Gas algorithm (SGNG) provides an interesting alternative to standard Multi-Layer Perceptrons (MLP). A comparison is drawn between the performance of SGNG and MLP in the domain of function mapping. A further field of interest is classification power, which has been investigated with real data taken by PS197 at CERN. The characteristics of the two network models will be discussed from a practical point of view as well as their advantages and disadvantages.