Evolving Systems

, Volume 2, Issue 1, pp 25–33 | Cite as

Improving premise structure in evolving Takagi–Sugeno neuro-fuzzy classifiers

Original Paper

Abstract

We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi–Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification on the overall classification performance.

Keywords

Incremental learning Takagi–Sugeno Neuro-fuzzy 

References

  1. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6(1):37–66Google Scholar
  2. Angelov P (2004) An approach for fuzzy rule-base adaptation using on-line clustering. Int J Approx Reason 35(3):275–289MATHCrossRefMathSciNetGoogle Scholar
  3. Angelov P (2010) Evolving Takagi-Sugeno fuzzy systems from streaming data, ets+. Evolving intelligent systems: methodology and applications, 21–50Google Scholar
  4. Angelov P, Filev D (2003) On-line design of Takagi-Sugeno models. In: IFSA’03: Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems, pp 576–584Google Scholar
  5. Angelov P, Filev D (2004) An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Trans Syst Man Cybern 34(1):484–498CrossRefGoogle Scholar
  6. Angelov P, Lughofer E, Zhou X (2008) Evolving fuzzy classifiers using different model architectures. Fuzzy Sets Syst 159(23):3160–3182MATHCrossRefMathSciNetGoogle Scholar
  7. Angelov P, Zhou X (2006) Evolving fuzzy systems from data streams in real-time. In: IEEE symposium on evolving fuzzy systemsGoogle Scholar
  8. Carpenter G, Grossberg S, Markuzon N, Reynolds J, Rosen D (1992) Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3:698–713Google Scholar
  9. Carpenter GA, Grossberg S (1988) The art of adaptive pattern recognition by a self-organizing neural network. Computer 21(3):77–88CrossRefGoogle Scholar
  10. De Backer S, Scheunders P (2001) Texture segmentation by frequency-sensitive elliptical competitive learning. Image Vis Comput 19(9–10):639–648CrossRefGoogle Scholar
  11. Frank A, Asuncion A (2010) UCI machine learning repository. http://archive.ics.uci.edu/ml
  12. Yen GG (2001) An effective neuro-fuzzy paradigm for machinery condition health monitoring. IEEE Trans Syst Man Cybern 31(4):523–536Google Scholar
  13. Jang JS (1993) Anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  14. Kasabov N (2007) Evolving connectionist systems: The knowledge engineering approach, 2nd edn. Springer, BerlinGoogle Scholar
  15. Littlestone N (1991) Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow. In: COLT ’91: Proceedings of the fourth annual workshop on Computational learning theory. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 147–156Google Scholar
  16. Lughofer E, (2008) Flexfis: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IIEEE Trans Fuzzy Systems 16(6):1393–1410CrossRefGoogle Scholar
  17. Mouchere H, Anquetil E, Ragot N (2007) On-line writer adaptation for handwriting recognition using fuzzy inference systems. Int J Pattern Recognit Artif Intell (IJPRAI) 21(1):99–116CrossRefGoogle Scholar
  18. Polikar R, Udpa L, Udpa S, Honavar V (2001) Learn++: an incremental learning algorithm for supervised neural networks. IEEE Trans Syst Man Cybern 31:497–508Google Scholar
  19. Reinke R, Michalski R (1988) Incremental learning of concept descriptions: a method and experimental results. In: Hayes J, Michie D, Richards J (eds) Machine Intelligence, vol 11, pp 263–288Google Scholar
  20. Sadri J, Suen CY, Bui TD (2006) A new clustering method for improving plasticity and stability in handwritten character recognition systems. Int Conf Pattern Recognit 2:1130–1133Google Scholar
  21. Song Q, Kasabov N (2000) Dynamic evolving neuro-fuzzy inference system (denfis): on-line learning and application for time-series prediction. IEEE Trans Fuzzy Syst 10:144–154Google Scholar
  22. Yager RR, Filev DP (1993) Learning of fuzzy rules by mountain clustering. SPIE 2061:246–254CrossRefGoogle Scholar
  23. Zwickel J, Wills AJ (2005) New directions in human associative learning. Psychology Press, Ch. Integrating associative models of supervised and unsupervised categorization, p 118Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.INSA de Rennes/UMR IRISARennesFrance

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