Instantaneous anomaly detection in online learning fuzzy systems
Autor(en): | Brockmann, W. Rosemann, N. |
Stichwörter: | Chaos theory; E-learning; Education; Financial data processing; Fuzzy logic; Fuzzy systems; Internet; Learning systems; Online systems, Anomaly detections; Automation systems; Closed loops; Local learning; Meta levels; Negative influence; On-line learning; Online learning systems; Self-optimizing; System behaviors; Worst case, Chaotic systems | Erscheinungsdatum: | 2008 | Journal: | 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS | Startseite: | 23 | Seitenende: | 28 | Zusammenfassung: | In the field of self-optimizing automation systems, incremental local learning is an important technique. But especially in case of closed loop coupling, learnt anomalies may have a negative influence on the entire future of the evolving system. In the worst case, this may result in unstable or chaotic system behavior. Thus it is crucial to detect anomalies in online learning systems instantaneously to be able to take immediate counteractions. This paper presents an intuitive approach how to detect anomalies in incrementally and locally learning TS-fuzzy systems by looking at local meta-level characteristics of the learnt function. The practical feasibility of this approach is then investigated in experiments with a real pole-balancing cart. © 2008 IEEE. |
Beschreibung: | Conference of 2008 3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS ; Conference Date: 4 March 2008 Through 7 March 2008; Conference Code:73060 |
ISBN: | 9781424416134 | DOI: | 10.1109/GEFS.2008.4484562 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-50149103918&doi=10.1109%2fGEFS.2008.4484562&partnerID=40&md5=c6df27bffe3268128dd721ea33ce4e43 |
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geprüft am 19.05.2024