T-S Fuzzy Modeling Based on Support Vector Learning

  • Wei Li
  • Yupu Yang
  • Zhong Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


This paper presents a satisfactory modeling method for data-driven fuzzy modeling problem based on support vector regression and Kalman filter algorithm. Support vector learning mechanism has been utilized to partition input data space to accomplish structure identification, then the complex model can be constructed by local linearization represented as T-S fuzzy model. For the ensuing parameter identification, we proceed with Kalman filter algorithm. Compared with previous works, the proposed approach guarantees the good accuracy and generalization capability especially in the few observations case. Numerical simulation results and comparisons with neuro-fuzzy method are discussed in order to assess the efficiency of the proposed approach.


Support Vector Machine Support Vector Fuzzy System Fuzzy Rule Support Vector Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and Applications to Modeling and Control, IEEE Transactions on System. Man, Cybernetic,116–132 (1985)Google Scholar
  2. 2.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference System. IEEE Transactions on Systems, Man, Cybernetics, 665–685 (1993)Google Scholar
  3. 3.
    Chiu, S.: Fuzzy Model Identification Based on Cluster Estimation. Journal of Intelligent and Fuzzy Systems, 267–278 (1994)Google Scholar
  4. 4.
    Azeem, M.F.: Structure Identification of Generalized Adaptive Neuro-Fuzzy Inference Systems. IEEE Transactions on Fuzzy Systems, 666–681 (2003)Google Scholar
  5. 5.
    Bastian, A.: Identifying Fuzzy Models Utilizing Genetic Programming. Fuzzy Sets and Systems, 333–350 (2000)Google Scholar
  6. 6.
    Pedrycz, W.: Evolutionary Fuzzy Modeling. IEEE Transactions on Fuzzy Systems, 652–665 (2003)Google Scholar
  7. 7.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  8. 8.
    A Tutorial on Support Vector Machines for Pattern Recognition. Christopher JC Burges, Knowledge Discovery and Data Mining, pp. 121–167 (1998)Google Scholar
  9. 9.
    Chen, Y.: Support Vector Learning for Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems, 716–728 (2003)Google Scholar
  10. 10.
    Chiang, J.-H.: Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach. IEEE Transactions on Fuzzy Systems, 1–12 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Li
    • 1
  • Yupu Yang
    • 1
  • Zhong Yang
    • 1
  1. 1.Institute of AutomationShanghai Jiaotong UniversityShanghaiChina

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