Class-modeling using Kohonen artificial neural networks

In this paper, a class-modeling technique based on Kohonen artificial neural networks is presented. In particular, in order for the Kohonen self-organizing map to operate as a class-modeling device, two main issues are identified: integrating the training set (composed of samples from a single category) with a set of uniformly distributed random vectors and computing a suitable probability distribution associated to the positions on the 2D layer of neurons. Both the identified features concur in defining an opportune class space. When used to analyze a real-world data set (classification of rice varieties), the proposed technique provided comparable and in some cases better results than the traditional chemometric techniques SIMCA and UNEQ.