OBSERVATIONS AND PROBLEMS APPLYING ART2 FOR DYNAMIC SENSOR PATTERN INTERPRETATION

This paper discusses characteristics of the ART2 (adaptive resonance t heory) information processing model which emerge when applied to the p roblem of interpreting dynamic sensor data. Fast learn ART2 is employe d in a supervised learning framework to classify process ''fingerprint s'' generated from multi-sensor trend patterns. Interest in ART2 was m otivated Level by the ability to provide closed classification regions , uniform hyperspherical clusters, feature extraction, and on-line ada ption. Temp Sensor data interpretation is briefly discussed with an em phasis on the unique attributes of the problem and the interaction wit h ART2 information processing principles. Pattern representations, e.g ., time domain, which encode information in both magnitude and directi on of the input vector are shown to be fundamentally incompatible with ART2. Complement coding is shown to solve this problem when the featu re extraction capability of the ART2 network is disabled. Complement c oding is also shown to preserve the clustering characteristics of the process ''fingerprints'' which are otherwise lost using the ART2 direc tional similarity measure. These issues are illustrated using an ART2- based monitoring system for a dynamically simulated chemical process.