Advertisement

Automatic Design of Hierarchical TS-FS Model Using Ant Programming and PSO Algorithm

  • Yuehui Chen
  • Jiwen Dong
  • Bo Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3192)

Abstract

This paper presents an approach for designing of hierarchical Takagi-Sugeno fuzzy system (TS-FS) automatically. The hierarchical structure is evolved using Ant Programming (AP) with specific instructions. The fine tuning of the rule’s parameters encoded in the structure is accomplished using Particle Swarm Optimization (PSO) algorithm. The proposed method interleaves both optimizations. Starting with random structures and rules’ parameters, it first tries to improve the hierarchical structure and then as soon as an improved structure is found, it fine tunes its rules’ parameters. It then goes back to improving the structure again and, provided it finds a better structure, it again fine tunes the rules’ parameters. This loop continues until a satisfactory solution (hierarchical Takagi-Sugeno fuzzy model) is found or a time limit is reached. The performance and effectiveness of the proposed method are evaluated using time series prediction problem and compared with the related methods.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man, Cybern. 15, 116–132 (1985)zbMATHGoogle Scholar
  2. 2.
    Brown, M., Bossley, K.M., Mills, D.J., Harris, C.J.: High dimensional neurofuzzy systems: overcoming the curse of dimensionality. In: Proc. 4th Int. Conf. on Fuzzy Systems, pp. 2139–2146 (1995)Google Scholar
  3. 3.
    Rainer, H.: Rule generation for hierarchical fuzzy systems. In: Proc. of the annual conf. of the North America Fuzzy Information Processing, pp. 444–449 (1997)Google Scholar
  4. 4.
    Wang, L.-X.: Analysis and design of hierarchical fuzzy systems. IEEE Trans. Fuzzy Systems 7, 617–624 (1999)CrossRefGoogle Scholar
  5. 5.
    Wang, L.-X.: Universal approximation by hierarchical fuzzy systems. Fuzzy Sets and Systems 93, 223–230 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Wei, C., Wang, L.-X.: A note on universal approximation by hierarchical fuzzy systems. Information Science 123, 241–248 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Lin, L.C., Lee, G.-Y.: Hierarchical fuzzy control for C-axis of CNC tuning centers using genetic algorithms. Journal of Intelligent and Robotic Systems 25, 255–275 (1999)zbMATHCrossRefGoogle Scholar
  8. 8.
    Duan, J.-C., Chung, F.-L.: Multilevel fuzzy relational systems: structure and identification. Soft Computing 6, 71–86 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Birattari, M., Di Caro, G., Dorigo, M.: Toward the formal foundation of Ant Programming. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 188–201. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Kennedy, J., et al.: Particle Swarm Optimization. In: Proc. of IEEE International Conference on Neural Networks. IV, pp. 1942–1948 (1995)Google Scholar
  11. 11.
    Kasabov, K., et al.: FuNN/2 - A fuzzy neural network architecture for adaptive learning and knowledge acquisition. Information Science 101, 155–175 (1997)CrossRefGoogle Scholar
  12. 12.
    Ying, H.: Theory and application of a novel fuzzy PID controller using a simplified Takagi-Sugeno rule scheme. Information Science 123, 281–293 (2000)CrossRefGoogle Scholar
  13. 13.
    Chen, Y., et al.: Nonlinear System Modeling via Optimal Design of Neural Trees. International Journal of Neural Systems 14, 125–137 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Yuehui Chen
    • 1
  • Jiwen Dong
    • 1
  • Bo Yang
    • 1
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R.China

Personalised recommendations