Enhancements to the dynamic self-organising neural network

Abstract

Class Directed Unsupervised Learning (CD UL) [7,8], having an input layer and a fully connected one-dimensional Kohonen layer which is used to conceptually separate data classes. These networks, along with CDUL, have all been developed from the Kohonen Self-Organising Map (SOM) [9,10] and use variations of the associated Kohonen learning rule. In each of these networks, the Kohonen nodes act as exemplars within an adaptive look-up table, and take on values corresponding to the centroids of each distinct data cluster in the feature space. Where CDUL differs is that the input layer is extended to include a class description of each training vector (see Fig. 1), and it is this additional information that provides CDUL with its interesting properties.The reason for the success of CDUL stems from two factors: firstly, the inclusion of class information as an integral section of the input vector, can be used to prevent Kohonen nodes from being trained by vectors of more than one class; and secondly, the size of the Kohonen layer can be computed