Tuning of Membership Functions

  • Shigeo Abe


In the previous chapter, we discuss extraction of the three types of fuzzy rules: those with pyramidal membership functions, those with polyhedral regions, and those with ellipsoidal regions using the data included in the associated clusters. Since these fuzzy rules are generated without considering the overlap between classes, their classification performance may not be good. Therefore to improve their classification performance, in this chapter we discuss tuning the slopes and the locations of the membership functions. The direct methods directly maximize the recognition rate of the training data by counting the net increase in the recognition rate when the slope or location of the membership function is changed [56, 72]. The indirect methods maximize the continuous objective function that leads to improving the recognition rate. Finally, we evaluate the performance of the tuning methods for the benchmark data.


Training Data Membership Function Recognition Rate Fuzzy Rule Fuzzy Classifier 
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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Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • Shigeo Abe
    • 1
  1. 1.Graduate School of Science and TechnologyKobe University, RokkodaiNada, KobeJapan

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