Learning behavior models for hybrid timed systems

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2012, Pages 1083-1090

  Niggemann, Oliver ^a   Stein, Benno ^b   Maier, Alexander ^c   Vodenčarević, Asmir ^d   Büning, Hans Kleine ^d  

^a FRAUNHOFER IOSB   (Germany)

^b BAUHAUS UNIVERSITY WEIMAR   (Germany)

^c Institute Industrial IT inIT   (Germany)

^d UNIVERSITY OF PADERBORN   (Germany)

Author keywords

Machine learning; Model formation; Simulation; Technical systems

Indexed keywords

ALGORITHMIC LEARNING; ANOMALY DETECTION; BEHAVIOR MODEL; DEVELOPMENT COSTS; FAULT IDENTIFICATIONS; LEARNING BEHAVIOR; LEARNING PROBLEM; MODE SWITCHES; OPEN LEARNING; PRODUCTION PLANT; PRODUCTION SYSTEM; RUNTIMES; SIMULATION; SYSTEM VARIABLES; TECHNICAL SYSTEMS; TIMED AUTOMATA; TIMED SYSTEMS;

ARTIFICIAL INTELLIGENCE; AUTOMATA THEORY; HYBRID SYSTEMS; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; LEARNING SYSTEMS; PRODUCTION ENGINEERING; QUALITY ASSURANCE; TIME SHARING SYSTEMS;

COMPUTER SIMULATION;

EID: 84868293556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Alur, R., and Dill, D. 1994. A theory of timed automata. Theoretical Computer Science vol. 126:183-235.
2. Buede, D. 2009. The Engineering Design of Systems: Models and Methods. John Wiley & Sons.
3. Carrasco, R. C., and Oncina, J. 1994. Learning stochastic regular grammars by means of a state merging method. In GRAMMATICAL INFERENCE AND APPLICATIONS, 139-152. Springer-Verlag.
4. Carrasco, R. C., and Oncina, J. 1999. Learning deterministic regular grammars from stochastic samples in polynomial time. In RAIRO (Theoretical Informatics and Applications). EnHiPro. Energie- und hilfsstoffoptimierte Produktion. www.enhipro.de. Laufzeit 06/2009-05/2012.
5. EWOTeK. Effizienzsteigerung von Werkzeugmaschinen durch Optimierung der Technologien zum Komponentenbetrieb. www.ewotek.de. Projektdauer: 01.07.2009 - 30.06.2012.
6. Fawcett, T. 2006. An introduction to roc analysis. Pattern Recogn. Lett. 27:861-874.
7. Gold, E. M. 1978. Complexity of automaton identification from given data. Information and Control 37(3):302-320.
8. Group, H.-L. 2006. MANUFUTURE - Strategic Research Agenda. Technical report, European Commission.
9. Henzinger, T. A.; Kopkeb, P. W.; Puria, A.; and Varaiya, P. 1998. What's decidable about hybrid automata? Journal of Computer and System Sciences 57(1).
10. Isermann, R. 2004. Model-based fault detection and diagnosis - status and applications. In 16th IFAC Symposium on Automatic Control in Aerospace.
11. Maier, A.; Vodenčarević, A.; Niggemann, O.; Just, R.; and Jäger, M. 2011. Anomaly detection in production plants using timed automata. In 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO).
12. Markou, M., and Singh, S. 2003a. Novelty detection: A review - part 1. Department of Computer Science, PANN Research, University of Exeter, United Kingdom.
13. Markou, M., and Singh, S. 2003b. Novelty detection: A review - part 2. Department of Computer Science, PANN Research, University of Exeter, United Kingdom.
14. Oncina, J., and Garcia, P. 1992. Inferring regular languages in polynomial update time. In Pattern Recognition and Image Analysis, volume 1 of Machine Perception and Artificial Intelligence. World Scientific,.
15. Pethig, F., and Niggemann, O. 2012. A process data acquisition architecture for distributed industrial networks. In Embedded World Conference.
16. Preuße, S., and Hanisch, H.-M. 2009. Specification of technical plant behavior with a safety-oriented technical language. In 7th IEEE International Conference on Industrial Informatics (INDIN), proceedings pp.632-637, Cardiff, United Kingdom.
17. Reber, A. S. 1967. Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior 6(6):855-863.
18. Schmidt, M., and Lipson, H. 2009. Distilling free-form natural laws from experimental data. SCIENCE 324.
19. Stein, B. 2001. Model Construction in Analysis and Synthesis Tasks. Habilitation, Department of Computer Science, University of Paderborn, Germany.
20. Thollard, F.; Dupont, P.; and de la Higuera, C. 2000. Probabilistic DFA inference using Kullback-Leibler divergence and minimality. In Proc. 17th International Conf. on Machine Learning, 975-982. Morgan Kaufmann.
21. VDI/VDE. 2009. Automation 2020 - bedeutung und entwicklung der automation bis zum jahr 2020.
22. Verwer, S.; de Weerdt, M.; and Witteveen, C. 2010. A likelihood-ratio test for identifying probabilistic deterministic real-time automata from positive data. In Sempere, J., and Garcia, P., eds., Grammatical Inference: Theoretical Results and Applications, Lecture Notes in Computer Science. Springer Berlin / Heidelberg.
23. Verwer, S. 2010. Efficient Identification of Timed Automata: Theory and Practice. Ph.D. Dissertation, Delft University of Technology.
24. Vodenčarević, A.; Büning, H. K.; Niggemann, O.; and Maier, A. 2011. Using behavior models for anomaly detection in hybrid systems. In Proceedings of the 23rd International Symposium on Information, Communication and Automation Technologies-ICAT 2011.
25. Witten, I., and Frank, E. 2005. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
26. Wymore, A. 1976. Systems Engineering for Interdisciplinary Teams. New York: John Wiley & Sons.
27. Zeigler, B.; Praehofer, H.; and Kim, T. 2000. Theory of Modeling and Simulation. New York: Academic Press.