Advertisement

Self-generating Interpretable Fuzzy Rules Model from Examples

  • Meng Li
  • Zhiwei Hu
  • Jiahong Liang
  • Shilei Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 324)

Abstract

In this paper, we propose a powerful method for automatically generating interpretable fuzzy rules model from a set of given training examples (i.e. numerical data) which are sampled from an unknown function. Self-generating fuzzy rules from examples can be used as a common method for simulation such as behavior simulation for virtual humans and CGF. Our method consists of two steps: Step 1 automatically extracts a fuzzy rule base which can approximate the unknown function with an approving accuracy by introducing a homologous Gaussian-shaped membership function. Step 2 improves its interpretability by deriving linguistic rules from fuzzy if-then rules with consequent real numbers. In this way, we achieve the balance between the accuracy and interpretability of the generated rules. Finally, we show the availability of our method by applying it to the problem of function approximation.

Keywords

Fuzzy modeling fuzzy rule rule extraction fuzzy system design orthogonal transformation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yen, J., Wang, L.: Simplifying Fuzzy Rule-Based Models Using Orthogonal Transformation Methods. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 29(1) (1999)Google Scholar
  2. 2.
    Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control. Springer, Berlin (1993)CrossRefzbMATHGoogle Scholar
  3. 3.
    Sudkamp, T., Hammell, R.J.: Interpolation, completion and learning fuzzy rules. IEEE Trans. Syst., Man, Cybern. 24, 332–342 (1994)CrossRefGoogle Scholar
  4. 4.
    Pomares, H., Rojas, I., Ortega, J., Gonzalez, J., Prieto, A.: A Systematic Approach to a Self-Generating Fuzzy Rule-Table for Function Approximation. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics 30(3) (2000)Google Scholar
  5. 5.
    Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270 (1997)CrossRefGoogle Scholar
  6. 6.
    Cherkassky, V., Gehring, D., Mulier, F.: Comparison of adaptive methods for function estimation from samples. IEEE Trans. Neural Networks 7, 969–984 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Meng Li
    • 1
  • Zhiwei Hu
    • 1
  • Jiahong Liang
    • 1
  • Shilei Li
    • 2
  1. 1.College of Mechanical Engineering and AutomationNational University of Defence TechnologyChangshaP.R. China
  2. 2.Department of Information Security, College of Electronic EngineeringNaval University of EngineeringWuhanP.R. China

Personalised recommendations