E. Lughofer∗ and C. Guardiola∗∗


  1. [1] J. Korbicz, J. Koscielny, Z. Kowalczuk, & W. Cholewa, Fault diagnosis – models, artificial intelligence and applications (Berlin, Heidelberg: Springer Verlag, 2004).
  2. [2] L. Chiang, E. Russell, & R. Braatz, Fault detection and diagnosis in industrial systems (London, Berlin, Heidelberg: Springer Verlag, 2001).
  3. [3] R. Duda, P. Hart, & D. Stork, Pattern classification, Second Edition (Chichester: Wiley-Interscience, 2000).
  4. [4] D. Swanson, Signal processing for intelligent sensors (Marcel Dekker, 2000).
  5. [5] E. Lughofer, H. Efendic, L. D. Re, & E. Klement, Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models, Proc. IEEE CDC–IEEE CDC Conf., Maui, Hawaii, 2003, 463–468.
  6. [6] S. Bay, K. Saito, N. Ueda, & P. Langley, A framework for discovering anomalous regimes in multivariate time-series data with local models, Symposium on Machine Learning for Anomaly Detection, Stanford, USA, 2004.
  7. [7] J. Chen & R. Patton, Robust model-based fault diagnosis for dynamic systems (Norwell, MA: Kluwer Academic Publishers, 1999).
  8. [8] S. Simani, C. Fantuzzi, & R. Patton, Model-based fault diagnosis in dynamic systems using identification techniques (Berlin, Heidelberg: Springer Verlag, 2002).
  9. [9] X. Li, H. Li, X. Guan, & R. Du, Fuzzy estimation of feedcutting force from current measurement – a case study on tool wear monitoring, IEEE Trans. Systems, Man, and Cybernetics Part C: Applications and Reviews, 34 (4), 2004, 506–512.
  10. [10] B. Samanta, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical Systems and Signal Processing, 18 (3), 2004, 625– 644.
  11. [11] X. Wang, U. Kruger, & B. Lennox, Recursive partial least squares algorithms for monitoring complex industrial processes, Control-Engineering-Practice, 11 (6), 2003, 613–632.
  12. [12] P. Albertos & A. Sala, Fault detection via continuous-time parameter estimation, Proc. IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS Helsinki Univ. Technol, Espoo, Finland, 1994, 87–92.
  13. [13] P. Angelov, V. Giglio, C. Guardiola, E. Lughofer, & J. Luján, An approach to model-based fault detection in industrial measurement systems with application to engine test benches, Measurement Science and Technology, 17, 2006, 1809–1818.
  14. [14] C.C.A. Likas, An incremental training method for the probabilistic rbf network, IEEE Transactions on Neural Networks, 17 (4), 2006, 966–974.
  15. [15] R. Andonie, L. Sasu, & V. Beiu, A modified fuzzy artmap architecture for incremental learning function approximation, In O. Castillo (Ed.), Neural networks and computational intelligence (Cancun, Mexico: Acta Press, 2003).
  16. [16] L. Ljung, System identification: theory for the user (Upper Saddle River, NJ: Prentice Hall PTR, Prentic Hall Inc., 1999).
  17. [17] P. Angelov & D. Filev, An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Trans. on Systems, Man and Cybernetics, Part B, 34 (1), 2004, 484–498.
  18. [18] N. K. Kasabov & Q. Song, DENFIS: Dynamic evolving neuralfuzzy inference system and its application for time-series prediction, IEEE Trans. on Fuzzy Systems, 10(2), 2002, 144–154.
  19. [19] E. Lughofer & E. Klement, FLEXFIS: A variant for incremental learning of Takagi-Sugeno fuzzy systems, Proceedings of FUZZIEEE 2005, Reno, Nevada, USA, 2005, 915–920.
  20. [20] L. Wang & J. Mendel, Fuzzy basis functions, universal approximation and orthogonal least-squares learning, IEEE Trans. Neural Networks, 3(5), 1992, 807–814.
  21. [21] E. Lughofer, E. Hüllermeier, & E. Klement, Improving the interpretability of data-driven evolving fuzzy systems, Proceedings of EUSFLAT 2005, Barcelona, Spain, 2005, 28–33.
  22. [22] R. Gray, Vector quantization, IEEE ASSP Magazine, 1984, 4–29.
  23. [23] E. Lughofer & U. Bodenhofer, Incremental learning of fuzzy basis function networks with a modified version of vector quantization, Proceedings of IPMU 2006, Volume 1, Paris, France, 2006, 56–63.
  24. [24] E. Lughofer, Process safety enhancements for data-driven evolving fuzzy models, Proceedings of 2nd Symposium on Evolving Fuzzy Systems, Lake District, UK, 2006, 42–48 (awarded as best paper).
  25. [25] T. Hastie, R. Tibshirani, & J. Friedman, The elements of statistical learning: Data mining, inference and prediction (New York, Berlin, Heidelberg: Springer Verlag, 2001).
  26. [26] N. Draper & H. Smith, Applied regression analysis. Probability and mathematical statistics (New York: John Wiley & Sons, 1981).
  27. [27] W. Groißböck, E. Lughofer, & E. Klement, A comparison of variable selection methods with the main focus on orthogonalization, Proceedings of SMPS Conference 2004, Oviedo, Spain, 2004. 316

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