Introduction

Self-organizing map (SOM): The self-organizing map belongs to a class of unsupervised learning that is based on competitive learning, in which output neurons compete amongst themselves to be activated, with the result that only one is activated at any one time. This activated neuron is called the winning neuron. Such competition can be induced/implemented by having lateral inhibition connections (negative feedback paths) between the neurons, resulting in the neurons organizing themselves. SOM can be imagined as a sheet-like neural network, with nodes arranged as regular, usually two-dimensional grids. The principal goal of a SOM is to transform an incoming arbitrary dimensional signal into a one- or two-dimensional discrete map, and to perform this transformation adaptively in a topologically ordered fashion. The neurons are selectively tuned to various input patterns (stimuli) or classes of input patterns during the course of the competitive learning. The locations of the...