Analysis of industrial systems using the self-organizing map

Abstract

The self-organizing map (SOM) is a neural network algorithm which is especially suitable for the analysis and visualization of high-dimensional data. It maps nonlinear statistical relationships between high-dimensional input data into simple geometric relationships, usually on a two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs in various engineering applications, in the analysis of complex processes or systems. In addition, SOM allows easy data fusion enabling visualization and analysis of large databases of industrial systems. As a case study, the SOM has been used to cluster the pulp and paper mills of the world.