Enforcing local properties in online learning first order TS-fuzzy systems by incremental regularization

2009 International Fuzzy Systems Association World Congress and 2009 European Society for Fuzzy Logic and Technology Conference, IFSA-EUSFLAT 2009 - Proceedings

Volumn , Issue , 2009, Pages 466-471

  Rosemann, Nils ^a   Brockmann, Werner ^a   Neumann, Bastian ^a  

^a UNIVERSITY OF OSNABRÜCK   (Germany)

Author keywords

First order Takagi Sugeno fuzzy systems; Fuzzy control; Incremental learning; Incremental regularization; Self optimization

Indexed keywords

COMPLEX APPLICATIONS; DESIGN TIME; FIRST ORDER; INCREMENTAL LEARNING; INCREMENTAL REGULARIZATION; LOCAL PROPERTY; ONE-FORM; ONLINE LEARNING; REGULARIZATION SCHEMES; SAFETY-CRITICAL; SELF-OPTIMIZATION; SELFTUNING; SMOOTH SYSTEM; SPECIAL CLASS; SYSTEM BEHAVIORS; TAKAGI SUGENO FUZZY SYSTEMS; ZERO ORDER;

FUZZY CONTROL; FUZZY LOGIC; FUZZY SYSTEMS;

E-LEARNING;

EID: 78549245760     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. C. Müller-Schloer, C. von der Malsburg, and R. P. Würtz. Organic Computing. Informatik-Spektrum, 27(4):332-336, 2004.
2. W. Brockmann. Online Machine Learning For Adaptive Control. IEEE Int. Work. Emerging Technologies and Factory Automation, 1992, pages 190-195, 1992.
3. J. Farrell and M. Polycarpou. Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches. Wiley Interscience, Hoboken, New Jersey, 2006.
4. P. P. Angelov and D. P. Filev. An Approach to Online Identification of Takagi-Sugeno Fuzzy Models. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 34(1):484-498, 2004.
5. T. A. Johansen and R. Murray-Smith. The operating regime approach to nonlinear modelling and control. Multiple Model Approaches to Modelling and Control, pages 3-72, 1997.
6. T. A. Johansen. Robust identification of Takagi-Sugeno-Kang fuzzy models using regularization. In Fifth IEEE Int. Conf. Fuzzy Systems, volume 1, pages 180-193, 1996.
7. J. Farrell and M. Polycarpou. Adaptive Approximation Based Control. Wiley-Interscience, 2006.
8. H.W. Engl, M. Hanke, and A. Neubauer. Regularization of Inverse Problems. Kluwer Academic Publishers, 1996.
9. F. Bauer, S. Pereverzev, and L. Rosasco. On regularization algorithms in learning theory. Journal of Complexity, 23(1):52-72, 2007.
10. A.E. Hoerl and R.W. Kennard. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 42(1):80-86, 2000.
11. A. Tikhonov. On the stability of inverse problems. CR (Dokl.) Acad. Sci. URSS, n. Ser., 39:176-179, 1943.
12. E. Lughofer and S. Kindermann. Improving the Robustness of Data-driven Fuzzy Systems with Regularization. In Proc. Int. Conf. Fuzzy Systems, pages 703-709. IEEE, 2008.
13. T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1):116-132, 1985.
14. W. Brockmann and A. Horst. Stabilizing the Convergence of Online-Learning in Neuro-Fuzzy Systems by an Immune System-inspired Approach. In Proc. IEEE Int. Conf. Fuzzy Systems, FUZZ-IEEE, pages 351-356. IEEE, 2007.
15. N. Rosemann and W. Brockmann. Concept for Controlled Self-optimization in Online Learning Neuro-fuzzy Systems. In J. Hertzberg, M. Beetz, and R. Englert, editors, KI 2007: Advances in Artificial Intelligence, LNAI, volume 4667, pages 498-501. Springer, Berlin, 2007.
16. N. Rosemann, B. Neumann, and W. Brockmann. Formale Eigenschaften des SILKE-Ansatzes zur Kontrolle selbstoptimierender Systeme. In H.-G. Hegering and A. et al. Lehmann, editors, Proc. Informatik 2008 Beherrschbare Systeme - dank Informatik, LNI, volume 134, pages 755-762. Gesellschaft für Informatik, Bonn, 2008.
17. W. Brockmann and N. Rosemann. Instantaneous Anomaly Detection in Online Learning Fuzzy-Systems. In F. Hoffmann and O. et al. Cordon, editors, 3rd Int. Workshop on Genetic and Evolving Fuzzy Systems, pages 23-28. IEEE Press, Piscataway, 2008.