Name
Abstract | ||
|---|---|---|
This paper discusses characteristics of the ART2 (adaptive resonance theory) information processing model which emerge when applied to the problem of interpreting dynamic sensor data. Fast learn ART2 is employed in a supervised learning framework to classify process “fingerprints” generated from multi-sensor trend patterns. Interest in ART2 was motivated by the ability to provide closed classification regions, uniform hyperspherical clusters, feature extraction, and on-line adaption. Sensor data interpretation is briefly discussed with an emphasis on the unique attributes of the problem and the interaction with ART2 information processing principles. Pattern representations, e.g., time domain, which encode information in both magnitude and direction of the input vector are shown to be fundamentally incompatible with ART2. Complement coding is shown to solve this problem when the feature extraction capability of the ART2 network is disabled. Complement coding is also shown to preserve the clustering characteristics of the process “fingerprints” which are otherwise lost using the ART2 directional similarity measure. These issues are illustrated using an ART2-based monitoring system for a dynamically simulated chemical process |
Year | DOI | Venue |
|---|---|---|
1996 | 10.1109/3468.508821 | IEEE Transactions on Systems, Man, and Cybernetics, Part A |
Keywords | Field | DocType |
sensor data interpretation,art2 directional similarity measure,feature extraction,feature extraction capability,encode information,dynamic sensor data,dynamic sensor pattern interpretation,art2 information processing principle,information processing model,dynamically simulated chemical process,art2 network,chemical processes,learning artificial intelligence,supervised learning,dynamic simulation,time domain,data interpretation,chemical engineering,adaptive resonance theory,information processing,resonance | Data mining,Similarity measure,Computer science,Coding (social sciences),Artificial intelligence,Cluster analysis,Information processing theory,Adaptive resonance theory,Information processing,Pattern recognition,Feature extraction,Supervised learning,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 4 | 1083-4427 |
Citations | PageRank | References |
13 | 1.28 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. R. Whiteley | 1 | 13 | 1.28 |
| J. F. Davis | 2 | 14 | 1.69 |
| A. Mehrotra | 3 | 13 | 1.28 |
| S. C. Ahalt | 4 | 114 | 13.12 |