Designing a Self-adaptive Union-Based Rule- Antecedent Fuzzy Controller Based on Two Step Optimization

  • Chang-Wook Han
  • Jung-Il Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


A self-adaptive union-based rule-antecedent fuzzy controller (SURFCon), which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The SURFCon allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the SURFCon, we consider the union-based logic processor (ULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, genetic algorithm (GA) constructs a Boolean skeleton of SURFCon, while stochastic reinforcement learning refines the binary connections of GA-optimized SURFCon for further improvement of the performance index. A cart-pole system is considered to verify the effectiveness of the proposed method.


Genetic Algorithm Rule Base Fuzzy Controller Connection Weight Fuzzy Neural Network 
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.
    Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    King, P.J., Mamdani, E.H.: The application of fuzzy control systems to industrial processes. Automatica 13(3), 235–242 (1977)CrossRefGoogle Scholar
  3. 3.
    Pal, S.K., King, R.A., Hashim, A.A.: Image description and primitive extraction using fuzzy set. IEEE Trans. Syst. Man and Cybern. SMC-13, 94–100 (1983)Google Scholar
  4. 4.
    Karr, C.L.: Design of an adaptive fuzzy logic controller using a genetic algorithm. In: Proc. Int. Conf. on Genetic Algorithms, pp. 450–457 (1991)Google Scholar
  5. 5.
    Homaifar, A., McCormick, E.: Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms. IEEE Trans. Fuzzy Systems 3(2), 129–139 (1995)CrossRefGoogle Scholar
  6. 6.
    Pedrycz, W., Reformat, M., Han, C.W.: Cascade architectures of fuzzy neural networks. Fuzzy Optimization and Decision Making 3(1), 5–37 (2004)MATHCrossRefGoogle Scholar
  7. 7.
    Xiong, N., Litz, L.: Reduction of fuzzy control rules by means of premise learning-method and case study. Fuzzy Sets and Systems 132(2), 217–231 (2002)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Pedrycz, W.: Fuzzy Neural Networks and Neurocomputations. Fuzzy Sets and Systems 56, 1–28 (1993)CrossRefGoogle Scholar
  9. 9.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  10. 10.
    Han, C.W., Park, J.I.: Design of a fuzzy controller using random signal-based learning employing simulated annealing. In: Proc. IEEE Conf. on Decision and Control, pp. 396–397 (2000)Google Scholar
  11. 11.
    Han, C.W., Park, J.I.: A study on hybrid random signal-based learning and its applications. Int. Jour. of Systems Science 35(4), 243–253 (2004)MATHCrossRefGoogle Scholar
  12. 12.
    Liu, B.D., Chen, C.Y., Tsao, J.Y.: Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms. IEEE Trans. Syst. Man and Cybern.-B 31(1), 32–53 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chang-Wook Han
    • 1
  • Jung-Il Park
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceYeungnam UniversityGyongbukSouth Korea

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