Self Organized Feature Maps for Monitoring and Knowledge Aquisition of a Chemical Process

Abstract

An application of Self-Organizing Feature Maps to the problem of process control for a chemical process is described. Very few of the nature and structure of the process can be learned from the trained Feature Map itself. A set of methods called U-Matrix methods have been developed to enhance the representation of data on Feature Maps. This methods allows to discover structure in the process data and allows to judge the quality of the learned maps. It can be used to extract knowledge for xpert systems from the Feature Maps. The extracted rules were able to control the chemical process as well as a human designed expert system. The methods presented here are in particular usefull to monitor critical processes and for the design of suitable human interfaces for process monitoring stands.